row_id string | section string | section_slug string | resource_type string | marker string | title string | url string | url_kind string | domain string | annotation string | description string | key_contribution string | novelty string | impact string | signal string | signal_strength string | source_readme string | source_line int64 | source_url string | date_added string | collection string | collection_slug string | user_goal string | lifecycle_stages string | audience string | loop_layer string | scope_fit string | evidence_class string | evidence_tier string | source_status string | canonical_url string | source_title string | source_description string | authors string | publication_date string | publication_year string | publication_venue string | publisher string | doi string | publication_note string | primary_category string | metadata_source string | github_repo string | github_stars string | github_forks string | github_license string | github_created_at string | github_updated_at string | arxiv_id string | audited_at timestamp[ms] |
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ale-0101 | Official Runtime Guides | official-runtime-guides | Blog | 📝 | Automate repository tasks with GitHub Agentic Workflows | https://github.blog/ai-and-ml/automate-repository-tasks-with-github-agentic-workflows/ | external | github.blog | Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/). | Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/). | Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/). | Primary-source operational guidance rather than commentary. Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/). | Use Automate repository tasks with GitHub Agentic Workflows to choose an implementation surface for repeatable agent work. | Contextual source from github.blog; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 664 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L664 | Build | build | Choose runtimes, tools, and delegation surfaces. | intake | builder | harness | enabling | practitioner-analysis | B | ok | https://github.blog/ai-and-ml/automate-repository-tasks-with-github-agentic-workflows/ | Automate repository tasks with GitHub Agentic Workflows - The GitHub Blog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon | Build automations using coding agents in GitHub Actions to handle triage, documentation, code quality, and more. | Don Syme, Peli de Halleux | 2026-02-13 | 2026 | The GitHub Blog | html-meta | 2026-08-31T13:57:28 | ||||||||||||
ale-0102 | Official Runtime Guides | official-runtime-guides | Blog | 📝 | Continuous AI in practice: What developers can automate today with agentic CI | https://github.blog/ai-and-ml/generative-ai/continuous-ai-in-practice-what-developers-can-automate-today-with-agentic-ci/ | external | github.blog | Concrete agentic-CI automations available today, with recurring patterns for triage, review, and documentation upkeep. | Concrete agentic-CI automations available today, with recurring patterns for triage, review, and documentation upkeep. | Concrete agentic-CI automations available today, with recurring patterns for triage, review, and documentation upkeep. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Concrete agentic-CI automations available today, with recurring patterns for triage, review, and documentation upkeep. | Use Continuous AI in practice: What developers can automate today with agentic CI to choose an implementation surface for repeatable agent work. | Contextual source from github.blog; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 665 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L665 | Build | build | Choose runtimes, tools, and delegation surfaces. | intake | builder | harness | enabling | practitioner-analysis | B | ok | https://github.blog/ai-and-ml/generative-ai/continuous-ai-in-practice-what-developers-can-automate-today-with-agentic-ci/ | Continuous AI in practice: What developers can automate today with agentic CI - The GitHub Blog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon | Think of Continuous AI as background agents that operate in your repository for tasks that require reasoning. | GitHub Staff | 2026-02-05 | 2026 | The GitHub Blog | html-meta | 2026-08-31T13:57:28 | ||||||||||||
ale-0103 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | About GitHub Copilot coding agent | https://docs.github.com/en/copilot/concepts/agents/coding-agent/about-coding-agent | external | docs.github.com | GitHub's autonomous coding agent: assign an issue, the agent works in an isolated Actions-powered workspace, and a reviewable pull request comes back. | GitHub's autonomous coding agent: assign an issue, the agent works in an isolated Actions-powered workspace, and a reviewable pull request comes back. | GitHub's autonomous coding agent: assign an issue, the agent works in an isolated Actions-powered workspace, and a reviewable pull request comes back. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. GitHub's autonomous coding agent: assign an issue, the agent works in an isolated Actions-powered workspace, and a reviewable pull request comes back. | Use About GitHub Copilot coding agent to choose an implementation surface for repeatable agent work. | Primary official documentation from docs.github.com; use it for current product or standard behavior. | high | README.md | 666 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L666 | Build | build | Choose runtimes, tools, and delegation surfaces. | intake;workspace | builder | harness | enabling | official-documentation | A | ok | https://docs.github.com/en/copilot/concepts/agents/cloud-agent/about-cloud-agent | About GitHub Copilot cloud agent - GitHub Docs | Copilot can research a repository, create an implementation plan, and make code changes on a branch. You can review the diff, iterate, and create a pull request when you're ready. | GitHub Docs | html-meta | 2026-08-31T13:57:28 | |||||||||||||||
ale-0104 | Official Runtime Guides | official-runtime-guides | Blog | 📝 | GitHub Copilot: Meet the new coding agent | https://github.blog/news-insights/product-news/github-copilot-meet-the-new-coding-agent/ | external | github.blog | Launch overview of the issue-to-PR delegation loop, including iteration on review feedback. | Launch overview of the issue-to-PR delegation loop, including iteration on review feedback. | Launch overview of the issue-to-PR delegation loop, including iteration on review feedback. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Launch overview of the issue-to-PR delegation loop, including iteration on review feedback. | Use GitHub Copilot: Meet the new coding agent to choose an implementation surface for repeatable agent work. | Contextual source from github.blog; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 667 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L667 | Build | build | Choose runtimes, tools, and delegation surfaces. | intake;delegation | builder | harness | enabling | practitioner-analysis | B | ok | https://github.blog/news-insights/product-news/github-copilot-meet-the-new-coding-agent/ | GitHub Copilot: Meet the new coding agent - The GitHub Blog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon | GitHub Copilot has a new feature: a coding agent that can implement a task or issue, run in the background with GitHub Actions, and more. | Thomas Dohmke | 2025-05-19 | 2025 | The GitHub Blog | html-meta | 2026-08-31T13:57:28 | ||||||||||||
ale-0105 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | GitHub Copilot for Jira Is Now Generally Available | https://github.blog/changelog/2026-06-25-github-copilot-for-jira-is-now-generally-available/ | external | github.blog | General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one. | General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one. | General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft p... | Use GitHub Copilot for Jira Is Now Generally Available to choose an implementation surface for repeatable agent work. | Primary official documentation from github.blog; use it for current product or standard behavior. | high | README.md | 668 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L668 | Build | build | Choose runtimes, tools, and delegation surfaces. | intake;delegation | builder | harness | enabling | official-documentation | A | ok | https://github.blog/changelog/2026-06-25-github-copilot-for-jira-is-now-generally-available/ | GitHub Copilot for Jira is now generally available - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon | GitHub Copilot for Jira is now generally available. Since launching the public preview in March 2026, we have shipped a series of enhancements based on your feedback, including model selection,… | 2026 | The GitHub Blog | html-meta | 2026-08-31T13:57:28 | ||||||||||||||
ale-0106 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Copilot Agent Session Streaming (Public Preview) | https://github.blog/changelog/2026-07-02-copilot-agent-session-streaming-is-now-in-public-preview/ | external | github.blog | Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work. | Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work. | Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving ent... | Use Copilot Agent Session Streaming (Public Preview) to choose an implementation surface for repeatable agent work. | Primary official documentation from github.blog; use it for current product or standard behavior. | high | README.md | 669 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L669 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;delegation | builder | harness | enabling | official-documentation | A | ok | https://github.blog/changelog/2026-07-02-copilot-agent-session-streaming-is-now-in-public-preview/ | Copilot agent session streaming is now in public preview - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon | GitHub Enterprise Cloud customers with enterprise managed users can now access GitHub Copilot agent session data across all Copilot clients, including: Cloud agents operating on github.com and data resident deployments… | 2026 | The GitHub Blog | html-meta | 2026-08-31T13:57:28 | ||||||||||||||
ale-0107 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Security Reviews in the GitHub Copilot App | https://github.blog/changelog/2026-07-14-security-reviews-now-available-in-the-github-copilot-app | external | github.blog | Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged. | Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged. | Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged. | Use Security Reviews in the GitHub Copilot App to choose an implementation surface for repeatable agent work. | Primary official documentation from github.blog; use it for current product or standard behavior. | high | README.md | 670 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L670 | 2026-07-15 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;delegation;state | builder | harness | enabling | official-documentation | A | ok | https://github.blog/changelog/2026-07-14-security-reviews-now-available-in-the-github-copilot-app/ | Security reviews now available in the GitHub Copilot app - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon | You can now run a security review on your in-flight code changes directly from the GitHub Copilot app. The /security-review slash command is shipping in public preview, bringing the same… | 2026 | The GitHub Blog | html-meta | 2026-08-31T13:57:28 | |||||||||||||
ale-0108 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Cursor cloud agents | https://cursor.com/docs/cloud-agent | external | cursor.com | Remote agents that work asynchronously in isolated environments and hand results back for review. | Remote agents that work asynchronously in isolated environments and hand results back for review. | Remote agents that work asynchronously in isolated environments and hand results back for review. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Remote agents that work asynchronously in isolated environments and hand results back for review. | Use Cursor cloud agents to choose an implementation surface for repeatable agent work. | Primary official documentation from cursor.com; use it for current product or standard behavior. | high | README.md | 675 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L675 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;delegation;state | builder | harness | enabling | official-documentation | A | ok | https://cursor.com/docs/cloud-agent | Cloud Agents | Cursor Docs Cursor Logo Cursor Logo | Run Agent in the cloud for continuous coding assistance. | Cursor Documentation | html-meta | 2026-08-31T13:57:28 | |||||||||||||||
ale-0109 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Cursor 3.8: Improvements to Cursor Automations | https://cursor.com/changelog/06-18-26 | external | cursor.com | Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents. | Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents. | Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub... | Use Cursor 3.8: Improvements to Cursor Automations to choose an implementation surface for repeatable agent work. | Primary official documentation from cursor.com; use it for current product or standard behavior. | high | README.md | 676 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L676 | Build | build | Choose runtimes, tools, and delegation surfaces. | trigger;workspace | builder | harness | enabling | official-documentation | A | ok | https://cursor.com/changelog/06-18-26 | Improvements to Cursor Automations · Cursor | Cursor | html-meta | 2026-08-31T13:57:28 | ||||||||||||||||
ale-0110 | Official Runtime Guides | official-runtime-guides | Blog | 📝 | Expanding Our Long-Running Agents Research Preview | https://cursor.com/blog/long-running-agents | external | cursor.com | Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines. | Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines. | Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as ... | Use Expanding Our Long-Running Agents Research Preview to choose an implementation surface for repeatable agent work. | Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 677 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L677 | Build | build | Choose runtimes, tools, and delegation surfaces. | escalation | builder | harness | enabling | practitioner-analysis | B | ok | https://cursor.com/blog/long-running-agents | Expanding our long-running agents research preview · Cursor | Long-running agents are now available in the Cursor web app for Ultra, Teams, and Enterprise users. | Cursor Team | Cursor | html-meta | 2026-08-31T13:57:28 | ||||||||||||||
ale-0111 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Cursor 3.11: Side Chats, Transcript Search, and Cloud Agent Hooks | https://cursor.com/changelog/side-chat | external | cursor.com | Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs. | Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs. | Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs. | The work separates roles across agents, verifiers, or orchestration layers. Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs. | Use Cursor 3.11: Side Chats, Transcript Search, and Cloud Agent Hooks to choose an implementation surface for repeatable agent work. | Primary official documentation from cursor.com; use it for current product or standard behavior. | high | README.md | 678 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L678 | Build | build | Choose runtimes, tools, and delegation surfaces. | delegation;exit | builder | harness | enabling | official-documentation | A | ok | https://cursor.com/changelog/side-chat | Side Chats and Conversation Search · Cursor | Cursor | html-meta | 2026-08-31T13:57:28 | ||||||||||||||||
ale-0112 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Jules | https://jules.google/docs | external | jules.google | Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs. | Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs. | Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs. | Use Jules to choose an implementation surface for repeatable agent work. | Primary official documentation from jules.google; use it for current product or standard behavior. | high | README.md | 683 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L683 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;delegation;state | builder | harness | enabling | official-documentation | A | ok | https://jules.google/docs | Getting started | Jules | Set up and run your first task with Jules | Jules | html-meta | 2026-08-31T13:57:28 | |||||||||||||||
ale-0113 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Devin Docs | https://docs.devin.ai/get-started/devin-intro | external | docs.devin.ai | Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries. | Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries. | Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries. | Use Devin Docs to choose an implementation surface for repeatable agent work. | Primary official documentation from docs.devin.ai; use it for current product or standard behavior. | high | README.md | 684 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L684 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;delegation;state | builder | harness | enabling | official-documentation | A | ok | https://docs.devin.ai/get-started/devin-intro | Introducing Devin - Devin Docs | Devin is the AI software engineer, built to help ambitious engineering teams crush their backlogs. | Devin Docs | html-meta | 2026-08-31T13:57:28 | |||||||||||||||
ale-0114 | Official Runtime Guides | official-runtime-guides | Blog | 📝 | Amp: Agents, Anywhere | https://ampcode.com/news/agents-anywhere | external | ampcode.com | Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI. | Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI. | Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI. | Use Amp: Agents, Anywhere to choose an implementation surface for repeatable agent work. | Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 685 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L685 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;delegation;state | builder | harness | enabling | practitioner-analysis | B | ok | https://ampcode.com/news/agents-anywhere | Agents, Anywhere - Amp | Remotely start agents anywhere you can run 'amp' | Amp | 2026-07-08 | 2026 | ampcode.com | html-meta | 2026-08-31T13:57:28 | ||||||||||||
ale-0115 | Official Runtime Guides | official-runtime-guides | Blog | 📝 | Amp: Right on Schedule | https://ampcode.com/news/schedule | external | ampcode.com | Amp adds scheduled agent runs, letting recurring work fire on a cadence with results reported back, moving the platform from on-demand sessions toward standing loops. | Amp adds scheduled agent runs, letting recurring work fire on a cadence with results reported back, moving the platform from on-demand sessions toward standing loops. | Amp adds scheduled agent runs, letting recurring work fire on a cadence with results reported back, moving the platform from on-demand sessions toward standing loops. | The trigger or cadence is explicit, making the workflow recurring rather than one-off. Amp adds scheduled agent runs, letting recurring work fire on a cadence with results reported back, moving the platform from on-demand sessions toward standing loops. | Use Amp: Right on Schedule to choose an implementation surface for repeatable agent work. | Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 686 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L686 | 2026-07-22 | Build | build | Choose runtimes, tools, and delegation surfaces. | trigger | builder | harness | enabling | practitioner-analysis | B | ok | https://ampcode.com/news/schedule | Right on Schedule - Amp | Agents can now set their own schedules, wake themselves up, and keep working. | Amp | 2026-07-21 | 2026 | ampcode.com | html-meta | 2026-08-31T13:57:28 | |||||||||||
ale-0116 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Claude Code What's New, Week 29 | https://code.claude.com/docs/en/whats-new/2026-w29 | external | code.claude.com | Weekly digest of Claude Code changes relevant to recurring agent work, following the Week 28 loop-integrity updates. | Weekly digest of Claude Code changes relevant to recurring agent work, following the Week 28 loop-integrity updates. | Weekly digest of Claude Code changes relevant to recurring agent work, following the Week 28 loop-integrity updates. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Weekly digest of Claude Code changes relevant to recurring agent work, following the Week 28 loop-integrity updates. | Use Claude Code What's New, Week 29 to choose an implementation surface for repeatable agent work. | Primary official documentation from code.claude.com; use it for current product or standard behavior. | high | README.md | 687 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L687 | 2026-07-22 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;delegation;state | builder | harness | enabling | official-documentation | A | ok | https://code.claude.com/docs/en/whats-new/2026-w29 | Week 29 · July 13–17, 2026 - Claude Code Docs | Pull live data into published artifacts through MCP connectors, and use Claude Code with a screen reader in the new screen reader mode. | 2026 | Claude Code Docs | html-meta | 2026-08-31T13:57:28 | |||||||||||||
ale-0117 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Copilot Code Review: Customization and Configurability | https://github.blog/changelog/2026-07-17-copilot-code-review-customization-and-configurability-improvements | external | github.blog | Changelog adding customization and configurability to Copilot code review, sharpening the automated review gate teams put between agent-written changes and merge. | Changelog adding customization and configurability to Copilot code review, sharpening the automated review gate teams put between agent-written changes and merge. | Changelog adding customization and configurability to Copilot code review, sharpening the automated review gate teams put between agent-written changes and merge. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Changelog adding customization and configurability to Copilot code review, sharpening the automated review gate teams put between agent-written changes and merge. | Use Copilot Code Review: Customization and Configurability to choose an implementation surface for repeatable agent work. | Primary official documentation from github.blog; use it for current product or standard behavior. | high | README.md | 688 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L688 | 2026-07-22 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;delegation;state | builder | harness | enabling | official-documentation | A | ok | https://github.blog/changelog/2026-07-17-copilot-code-review-customization-and-configurability-improvements/ | Copilot code review: Customization and configurability improvements - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon | Copilot code review now utilizes a firewall, custom setup steps, and independent runner configurations. It now reads custom instructions from the head branch to allow for easy testing and validation… | 2026 | The GitHub Blog | html-meta | 2026-08-31T13:57:28 | |||||||||||||
ale-0118 | Official Runtime Guides | official-runtime-guides | Blog | 📝 | Amp: Event Driven Orbs | https://ampcode.com/news/event-driven-orbs | external | ampcode.com | Amp's July 23, 2026 launch letting orbs react to outside events: a plugin registers a durable webhook endpoint, each incoming event's signature is verified, and the configured handler runs in an orb thread seeded with trusted event metadata, turning GitHub CI failures, Linear issues, and Discord messages into event-tri... | Amp's July 23, 2026 launch letting orbs react to outside events: a plugin registers a durable webhook endpoint, each incoming event's signature is verified, and the configured handler runs in an orb thread seeded with trusted event metadata, turning GitHub CI failures, Linear issues, and Discord messages into event-tri... | Amp's July 23, 2026 launch letting orbs react to outside events: a plugin registers a durable webhook endpoint, each incoming event's signature is verified, and the configured handler runs in an orb thread seeded with trusted event metadata, turning GitHub CI failures, Linear issues, and Discord messages into event-tri... | Durable execution and replay are treated as first-class loop infrastructure. Amp's July 23, 2026 launch letting orbs react to outside events: a plugin registers a durable webhook endpoint, each incoming event's signature is verified, and the configured handler runs in an orb thread seeded with trusted event metadata, t... | Use Amp: Event Driven Orbs to choose an implementation surface for repeatable agent work. | Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 689 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L689 | 2026-07-24 | Build | build | Choose runtimes, tools, and delegation surfaces. | trigger;intake;workspace;verification | builder | harness | enabling | practitioner-analysis | B | ok | https://ampcode.com/news/event-driven-orbs | Event Driven Orbs - Amp | Orbs can now receive requests and react to outside events. | Amp | 2026-07-23 | 2026 | ampcode.com | html-meta | 2026-08-31T13:57:28 | |||||||||||
ale-0119 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Scan Your Codebase for Vulnerabilities | https://code.claude.com/docs/en/claude-security | external | code.claude.com | Anthropic's Claude Security plugin runs a multi-agent deep scan of a repository or diff inside a Claude Code session: dynamic workflows orchestrate agents that map the architecture, build a threat model, and hunt vulnerabilities, with independent verifier agents gating every finding before it reaches the report. | Anthropic's Claude Security plugin runs a multi-agent deep scan of a repository or diff inside a Claude Code session: dynamic workflows orchestrate agents that map the architecture, build a threat model, and hunt vulnerabilities, with independent verifier agents gating every finding before it reaches the report. | Anthropic's Claude Security plugin runs a multi-agent deep scan of a repository or diff inside a Claude Code session: dynamic workflows orchestrate agents that map the architecture, build a threat model, and hunt vulnerabilities, with independent verifier agents gating every finding before it reaches the report. | Verification is promoted from a final check to a loop-control signal. Anthropic's Claude Security plugin runs a multi-agent deep scan of a repository or diff inside a Claude Code session: dynamic workflows orchestrate agents that map the architecture, build a threat model, and hunt vulnerabilities, with independent ver... | Use Scan Your Codebase for Vulnerabilities to choose an implementation surface for repeatable agent work. | Primary official documentation from code.claude.com; use it for current product or standard behavior. | high | README.md | 690 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L690 | 2026-07-24 | Build | build | Choose runtimes, tools, and delegation surfaces. | delegation | builder | harness | enabling | official-documentation | A | ok | https://code.claude.com/docs/en/claude-security | Scan your codebase for vulnerabilities - Claude Code Docs | Install the Claude Security plugin to scan your codebase for vulnerabilities in a Claude Code session and turn findings into patches you review and apply. | Claude Code Docs | html-meta | 2026-08-31T13:57:28 | ||||||||||||||
ale-0120 | Official Runtime Guides | official-runtime-guides | Blog | 📝 | Agent Automation Controls in GitHub Issues | https://github.blog/changelog/2026-07-23-agent-automation-controls-in-github-issues-in-public-preview | external | github.blog | Public preview that puts approval gates on autonomous issue triage: agents driven by Agentic Workflows or the Copilot cloud agent suggest label, field, issue-type, assignee, and close actions with a stated rationale, high-confidence actions apply automatically while lower-confidence ones queue for human accept/decline,... | Public preview that puts approval gates on autonomous issue triage: agents driven by Agentic Workflows or the Copilot cloud agent suggest label, field, issue-type, assignee, and close actions with a stated rationale, high-confidence actions apply automatically while lower-confidence ones queue for human accept/decline,... | Public preview that puts approval gates on autonomous issue triage: agents driven by Agentic Workflows or the Copilot cloud agent suggest label, field, issue-type, assignee, and close actions with a stated rationale, high-confidence actions apply automatically while lower-confidence ones queue for human accept/decline,... | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Public preview that puts approval gates on autonomous issue triage: agents driven by Agentic Workflows or the Copilot cloud agent suggest label, field, issue-type, assignee, and close actions with a... | Use Agent Automation Controls in GitHub Issues to choose an implementation surface for repeatable agent work. | Contextual source from github.blog; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 691 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L691 | 2026-07-24 | Build | build | Choose runtimes, tools, and delegation surfaces. | intake;escalation | builder | harness | enabling | practitioner-analysis | B | ok | https://github.blog/changelog/2026-07-23-agent-automation-controls-in-github-issues-in-public-preview/ | Agent automation controls in GitHub Issues in public preview - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon | Agent automations increasingly label, type, assign, and close issues for you. GitHub Issues now shows the reason behind each change and lets you review them before they’re applied, so you… | 2026 | The GitHub Blog | html-meta | 2026-08-31T13:57:28 | |||||||||||||
ale-0121 | Official Runtime Guides | official-runtime-guides | Blog | 📝 | Copilot Cloud Agent for Linear Is Now Generally Available | https://github.blog/changelog/2026-07-23-copilot-cloud-agent-for-linear-is-now-generally-available | external | github.blog | General availability of Copilot cloud agent for Linear: assign a Linear issue to the agent, which analyzes it, opens a draft pull request from an ephemeral GitHub Actions environment, streams progress to the Linear activity timeline, and accepts mid-task steering via comments, with model selection, custom agent, and br... | General availability of Copilot cloud agent for Linear: assign a Linear issue to the agent, which analyzes it, opens a draft pull request from an ephemeral GitHub Actions environment, streams progress to the Linear activity timeline, and accepts mid-task steering via comments, with model selection, custom agent, and br... | General availability of Copilot cloud agent for Linear: assign a Linear issue to the agent, which analyzes it, opens a draft pull request from an ephemeral GitHub Actions environment, streams progress to the Linear activity timeline, and accepts mid-task steering via comments, with model selection, custom agent, and br... | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. General availability of Copilot cloud agent for Linear: assign a Linear issue to the agent, which analyzes it, opens a draft pull request from an ephemeral GitHub Actions environment, streams progre... | Use Copilot Cloud Agent for Linear Is Now Generally Available to choose an implementation surface for repeatable agent work. | Contextual source from github.blog; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 692 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L692 | 2026-07-24 | Build | build | Choose runtimes, tools, and delegation surfaces. | intake | builder | harness | enabling | practitioner-analysis | B | ok | https://github.blog/changelog/2026-07-23-copilot-cloud-agent-for-linear-is-now-generally-available/ | Copilot cloud agent for Linear is now generally available - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon | You can now assign issues in Linear to Copilot cloud agent, our asynchronous, autonomous background agent. When you assign a Linear issue to Copilot, it will: Analyze the issue contents… | 2026 | The GitHub Blog | html-meta | 2026-08-31T13:57:28 | |||||||||||||
ale-0122 | Official Runtime Guides | official-runtime-guides | Blog | 📝 | The New Rules of Context Engineering for Claude 5 Generation Models | https://claude.com/blog/the-new-rules-of-context-engineering-for-claude-5-generation-models | external | claude.com | Official Anthropic post (Jul 24, 2026) by Thariq Shihipar (MTS): Anthropic removed over 80% of Claude Code's system prompt for Opus 5/Fable 5 with no measurable eval loss, and lays out five shifts, rules-to-judgment, example-to-interface tool design, progressive disclosure, concise tool descriptions, and manual-to-auto... | Official Anthropic post (Jul 24, 2026) by Thariq Shihipar (MTS): Anthropic removed over 80% of Claude Code's system prompt for Opus 5/Fable 5 with no measurable eval loss, and lays out five shifts, rules-to-judgment, example-to-interface tool design, progressive disclosure, concise tool descriptions, and manual-to-auto... | Official Anthropic post (Jul 24, 2026) by Thariq Shihipar (MTS): Anthropic removed over 80% of Claude Code's system prompt for Opus 5/Fable 5 with no measurable eval loss, and lays out five shifts, rules-to-judgment, example-to-interface tool design, progressive disclosure, concise tool descriptions, and manual-to-auto... | Primary-source operational guidance rather than commentary. Official Anthropic post (Jul 24, 2026) by Thariq Shihipar (MTS): Anthropic removed over 80% of Claude Code's system prompt for Opus 5/Fable 5 with no measurable eval loss, and lays out five shifts, rules-to-judgment, example-to-interface tool design, progressi... | Use The New Rules of Context Engineering for Claude 5 Generation Models to choose an implementation surface for repeatable agent work. | Contextual source from claude.com; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 693 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L693 | 2026-07-25 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;verification | builder | harness | enabling | practitioner-analysis | B | ok | https://claude.com/blog/the-new-rules-of-context-engineering-for-claude-5-generation-models | The new rules of context engineering for Claude 5 generation models | Claude by Anthropic | We removed over 80% of Claude Code's system prompt for more advanced models. How to apply the lessons we learned to your own context engineering in Claude Code and with your own agents. | Claude | html-meta | 2026-08-31T13:57:28 | ||||||||||||||
ale-0123 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Claude Cookbook | https://platform.claude.com/cookbook/ | external | platform.claude.com | Anthropic's hosted cookbook of runnable recipes for building with Claude, including agent loops with tool use, memory management, context editing, and evaluation harnesses, kept current with each model generation as the reference implementation source. | Anthropic's hosted cookbook of runnable recipes for building with Claude, including agent loops with tool use, memory management, context editing, and evaluation harnesses, kept current with each model generation as the reference implementation source. | Anthropic's hosted cookbook of runnable recipes for building with Claude, including agent loops with tool use, memory management, context editing, and evaluation harnesses, kept current with each model generation as the reference implementation source. | Evaluation data is used as the feedback signal for improving loop behavior. Anthropic's hosted cookbook of runnable recipes for building with Claude, including agent loops with tool use, memory management, context editing, and evaluation harnesses, kept current with each model generation as the reference implementation... | Use Claude Cookbook to choose an implementation surface for repeatable agent work. | Primary official documentation from platform.claude.com; use it for current product or standard behavior. | high | README.md | 694 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L694 | 2026-07-25 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;verification | builder | harness | enabling | official-documentation | A | ok | https://platform.claude.com/cookbook/ | Claude Cookbook | Practical guides and code examples for building with Claude. Learn prompting techniques, tool use, multimodal capabilities, and more. | Claude Cookbook | html-meta | 2026-08-31T13:57:28 | ||||||||||||||
ale-0124 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | The 2026-07-28 MCP Specification | https://blog.modelcontextprotocol.io/posts/2026-07-28/ | external | blog.modelcontextprotocol.io | Official MCP release announcement (Jul 28, 2026) by lead maintainers David Soria Parra and Den Delimarsky, publishing the 2026-07-28 spec as final after a ten-week release-candidate validation window. | Official MCP release announcement (Jul 28, 2026) by lead maintainers David Soria Parra and Den Delimarsky, publishing the 2026-07-28 spec as final after a ten-week release-candidate validation window. | Official MCP release announcement (Jul 28, 2026) by lead maintainers David Soria Parra and Den Delimarsky, publishing the 2026-07-28 spec as final after a ten-week release-candidate validation window. | Primary-source operational guidance rather than commentary. Official MCP release announcement (Jul 28, 2026) by lead maintainers David Soria Parra and Den Delimarsky, publishing the 2026-07-28 spec as final after a ten-week release-candidate validation window. | Use The 2026-07-28 MCP Specification to choose an implementation surface for repeatable agent work. | Primary official documentation from blog.modelcontextprotocol.io; use it for current product or standard behavior. | high | README.md | 695 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L695 | 2026-07-28 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;delegation;state | builder | harness | enabling | official-documentation | A | ok | https://blog.modelcontextprotocol.io/posts/2026-07-28/ | The 2026-07-28 Specification | Model Context Protocol Blog | The 2026-07-28 Model Context Protocol specification is out, bringing a stateless protocol core, Multi Round-Trip Requests, header-based routing, cacheable list results, authorization hardening, a formal extensions framework, and updated Tier 1 SDKs. | David Soria Parra (Lead Maintainer), Den Delimarsky (Lead Maintainer) | 2026-07-28 | 2026 | Model Context Protocol Blog | html-meta | 2026-08-31T13:57:28 | |||||||||||
ale-0125 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | MCP Tasks Extension | https://modelcontextprotocol.io/extensions/tasks/overview | external | modelcontextprotocol.io | Normative documentation for io.modelcontextprotocol/tasks (SEP-2663), promoted out of experimental core into an official extension with the 2026-07-28 spec and redesigned around durable handles instead of session-bound objects. | Normative documentation for io.modelcontextprotocol/tasks (SEP-2663), promoted out of experimental core into an official extension with the 2026-07-28 spec and redesigned around durable handles instead of session-bound objects. | Normative documentation for io.modelcontextprotocol/tasks (SEP-2663), promoted out of experimental core into an official extension with the 2026-07-28 spec and redesigned around durable handles instead of session-bound objects. | Primary-source operational guidance rather than commentary. Normative documentation for io.modelcontextprotocol/tasks (SEP-2663), promoted out of experimental core into an official extension with the 2026-07-28 spec and redesigned around durable handles instead of session-bound objects. | Use MCP Tasks Extension to choose an implementation surface for repeatable agent work. | Primary official documentation from modelcontextprotocol.io; use it for current product or standard behavior. | high | README.md | 696 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L696 | 2026-07-28 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;delegation;state | builder | harness | enabling | official-documentation | A | ok | https://modelcontextprotocol.io/extensions/tasks/overview | Tasks - Model Context Protocol | Asynchronous task execution for long-running MCP operations | Model Context Protocol | html-meta | 2026-08-31T13:57:28 | ||||||||||||||
ale-0126 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Antigravity CLI | https://github.com/google-antigravity/antigravity-cli | external | github.com | Google's Antigravity CLI release of Jul 28, 2026 turns the agent into a programmable component rather than a terminal session. | Google's Antigravity CLI release of Jul 28, 2026 turns the agent into a programmable component rather than a terminal session. | Google's Antigravity CLI release of Jul 28, 2026 turns the agent into a programmable component rather than a terminal session. | Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Google's Antigravity CLI release of Jul 28, 2026 turns the agent into a programmable component rather than a terminal session. | Use Antigravity CLI to choose an implementation surface for repeatable agent work. | Primary official documentation from github.com; use it for current product or standard behavior. | high | README.md | 697 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L697 | 2026-07-28 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;delegation;state | builder | harness | enabling | official-documentation | A | ok | https://github.com/google-antigravity/antigravity-cli | GitHub - google-antigravity/antigravity-cli: Antigravity CLI brings the reasoning, execution, and orchestration capabilities of Antigravity agent harness directly into your terminal. · GitHub | Antigravity CLI brings the reasoning, execution, and orchestration capabilities of Antigravity agent harness directly into your terminal. - google-antigravity/antigravity-cli | google-antigravity/antigravity-cli | GitHub | html-meta | google-antigravity/antigravity-cli | 2026-08-31T13:57:28 | ||||||||||||
ale-0127 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Enterprise Managed Settings Now Apply to the GitHub Copilot App | https://github.blog/changelog/2026-07-27-enterprise-managed-settings-now-apply-to-the-github-copilot-app | external | github.blog | GitHub changelog (Jul 27, 2026) extending managed-settings.json governance, previously limited to interactive CLI and VS Code clients, to the Copilot cloud agent, the autonomous background worker that opens PRs from issue assignments. | GitHub changelog (Jul 27, 2026) extending managed-settings.json governance, previously limited to interactive CLI and VS Code clients, to the Copilot cloud agent, the autonomous background worker that opens PRs from issue assignments. | GitHub changelog (Jul 27, 2026) extending managed-settings.json governance, previously limited to interactive CLI and VS Code clients, to the Copilot cloud agent, the autonomous background worker that opens PRs from issue assignments. | The contribution is machine-readable and validation-friendly. GitHub changelog (Jul 27, 2026) extending managed-settings.json governance, previously limited to interactive CLI and VS Code clients, to the Copilot cloud agent, the autonomous background worker that opens PRs from issue assignments. | Use Enterprise Managed Settings Now Apply to the GitHub Copilot App to choose an implementation surface for repeatable agent work. | Primary official documentation from github.blog; use it for current product or standard behavior. | high | README.md | 698 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L698 | 2026-07-28 | Build | build | Choose runtimes, tools, and delegation surfaces. | intake | builder | harness | enabling | official-documentation | A | ok | https://github.blog/changelog/2026-07-27-enterprise-managed-settings-now-apply-to-the-github-copilot-app/ | Enterprise managed settings in the GitHub Copilot app and Copilot cloud agent - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon | You can now govern the GitHub Copilot app and Copilot cloud agent with enterprise managed settings, the same centrally managed policies you use to control Copilot across your enterprise. With… | 2026 | The GitHub Blog | html-meta | 2026-08-31T13:57:28 | |||||||||||||
ale-0128 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | GitHub Copilot in Visual Studio Code, July 2026 Releases | https://github.blog/changelog/2026-07-30-github-copilot-in-visual-studio-code-july-2026-releases/ | external | github.blog | The Agents window (public preview) gets the observability and isolation primitives that parallel agent operation needs. Sessions can now be started in a Git worktree, and notably for Copilot, Claude, or Codex sessions, not just Copilot's own, giving each concurrent agent its own checkout instead of contending over one ... | The Agents window (public preview) gets the observability and isolation primitives that parallel agent operation needs. Sessions can now be started in a Git worktree, and notably for Copilot, Claude, or Codex sessions, not just Copilot's own, giving each concurrent agent its own checkout instead of contending over one ... | The Agents window (public preview) gets the observability and isolation primitives that parallel agent operation needs. Sessions can now be started in a Git worktree, and notably for Copilot, Claude, or Codex sessions, not just Copilot's own, giving each concurrent agent its own checkout instead of contending over one ... | Workspace isolation is part of the loop design, not an afterthought. The Agents window (public preview) gets the observability and isolation primitives that parallel agent operation needs. Sessions can now be started in a Git worktree, and notably for Copilot, Claude, or Codex sessions, not just Copilot's own, giving e... | Use GitHub Copilot in Visual Studio Code, July 2026 Releases to choose an implementation surface for repeatable agent work. | Primary official documentation from github.blog; use it for current product or standard behavior. | high | README.md | 699 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L699 | 2026-08-02 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace | builder | harness | enabling | official-documentation | A | ok | https://github.blog/changelog/2026-07-30-github-copilot-in-visual-studio-code-july-2026-releases/ | GitHub Copilot in Visual Studio Code, July 2026 releases - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon | This changelog covers VS Code v1.127 through v1.131, shipped throughout July 2026. These releases improve how you work with agents, review changes, use chat, and navigate VS Code. They also… | 2026 | The GitHub Blog | html-meta | 2026-08-31T13:57:28 | |||||||||||||
ale-0129 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Durable Workflows in Microsoft Agent Framework | https://devblogs.microsoft.com/dotnet/durable-workflows-in-microsoft-agent-framework/ | external | devblogs.microsoft.com | Adds durability to an agent workflow through hosting configuration rather than a rewrite: each executor becomes a durable activity with automatic retry, progress is checkpointed after every step, and a resumed run reads completed executors from the checkpoint store instead of re-running them. | Adds durability to an agent workflow through hosting configuration rather than a rewrite: each executor becomes a durable activity with automatic retry, progress is checkpointed after every step, and a resumed run reads completed executors from the checkpoint store instead of re-running them. | Adds durability to an agent workflow through hosting configuration rather than a rewrite: each executor becomes a durable activity with automatic retry, progress is checkpointed after every step, and a resumed run reads completed executors from the checkpoint store instead of re-running them. | Durable execution and replay are treated as first-class loop infrastructure. Adds durability to an agent workflow through hosting configuration rather than a rewrite: each executor becomes a durable activity with automatic retry, progress is checkpointed after every step, and a resumed run reads completed executors fro... | Use Durable Workflows in Microsoft Agent Framework to choose an implementation surface for repeatable agent work. | Primary official documentation from devblogs.microsoft.com; use it for current product or standard behavior. | high | README.md | 700 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L700 | 2026-08-13 | Build | build | Choose runtimes, tools, and delegation surfaces. | state;budget | builder | harness | enabling | official-documentation | A | ok | https://devblogs.microsoft.com/dotnet/durable-workflows-in-microsoft-agent-framework/ | Durable Workflows in the Microsoft Agent Framework - .NET Blog Your Privacy Choices Opt-Out Icon Your Privacy Choices Opt-Out Icon | Build durable AI agent workflows with the Microsoft Agent Framework. Define once, run in-process or on Azure Functions with serverless scaling and durability. | Shyju Krishnankutty | 2026-05-06 | 2026 | .NET Blog | html-meta | 2026-08-31T13:57:28 | |||||||||||
ale-0130 | Official Runtime Guides | official-runtime-guides | Docs | 📚 | Context Engineering in Deep Agents | https://docs.langchain.com/oss/python/deepagents/context-engineering | external | docs.langchain.com | Documents the thresholds a production harness actually needs. Tool payloads over 20,000 tokens are written to the filesystem and replaced by a reference, summarization and offloading fire at 85% of the context window, subagents return only results, and a composite backend routes /memories to durable cross-thread storag... | Documents the thresholds a production harness actually needs. Tool payloads over 20,000 tokens are written to the filesystem and replaced by a reference, summarization and offloading fire at 85% of the context window, subagents return only results, and a composite backend routes /memories to durable cross-thread storag... | Documents the thresholds a production harness actually needs. Tool payloads over 20,000 tokens are written to the filesystem and replaced by a reference, summarization and offloading fire at 85% of the context window, subagents return only results, and a composite backend routes /memories to durable cross-thread storag... | Durable execution and replay are treated as first-class loop infrastructure. Documents the thresholds a production harness actually needs. Tool payloads over 20,000 tokens are written to the filesystem and replaced by a reference, summarization and offloading fire at 85% of the context window, subagents return only res... | Use Context Engineering in Deep Agents to choose an implementation surface for repeatable agent work. | Primary official documentation from docs.langchain.com; use it for current product or standard behavior. | high | README.md | 701 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L701 | 2026-08-13 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;delegation;budget | builder | harness | enabling | official-documentation | A | ok | https://docs.langchain.com/oss/python/deepagents/context-engineering | Context engineering in Deep Agents - Docs by LangChain | Control what context your deep agent has access to and how it is managed across long-running tasks | Docs by LangChain | html-meta | 2026-08-31T13:57:28 | ||||||||||||||
ale-0131 | Research Foundations | research-foundations | Paper | 📄 | ReAct: Synergizing Reasoning and Acting in Language Models | https://arxiv.org/abs/2210.03629 | external | arxiv.org | Foundational reason-act-observe loop for tool-using language agents. | Foundational reason-act-observe loop for tool-using language agents. | Foundational reason-act-observe loop for tool-using language agents. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Foundational reason-act-observe loop for tool-using language agents. | Use ReAct: Synergizing Reasoning and Acting in Language Models to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2210.03629; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 709 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L709 | Learn | learn | Understand the field and its boundaries. | workspace | researcher;evaluator | cross-layer | enabling | research-paper | A | ok | https://openreview.net/forum?id=WE_vluYUL-X | [2210.03629] ReAct: Synergizing Reasoning and Acting in Language Models | While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we ... | Shunyu Yao; Jeffrey Zhao; Dian Yu; Nan Du; Izhak Shafran; Karthik Narasimhan; Yuan Cao | 2023 | 2023 | International Conference on Learning Representations (ICLR) | International Conference on Learning Representations | Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access. | cs.CL | OpenReview proceedings record | 2210.03629 | 2026-08-31T13:57:28 | ||||||||
ale-0132 | Research Foundations | research-foundations | Paper | 📄 | Reflexion: Language Agents with Verbal Reinforcement Learning | https://arxiv.org/abs/2303.11366 | external | arxiv.org | Converts environment feedback into written reflections stored in memory for future attempts. | Converts environment feedback into written reflections stored in memory for future attempts. | Converts environment feedback into written reflections stored in memory for future attempts. | Persistent memory is treated as an external runtime artifact. Converts environment feedback into written reflections stored in memory for future attempts. | Use Reflexion: Language Agents with Verbal Reinforcement Learning to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2303.11366; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 710 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L710 | Learn | learn | Understand the field and its boundaries. | context | researcher;evaluator | cross-layer | enabling | research-paper | A | ok | https://proceedings.neurips.cc/paper_files/paper/2023/hash/1b44b878bb782e6954cd888628510e90-Abstract-Conference.html | [2303.11366] Reflexion: Language Agents with Verbal Reinforcement Learning | Large language models (LLMs) have been increasingly used to interact with external environments (e.g., games, compilers, APIs) as goal-driven agents. However, it remains challenging for these language agents to quickly and efficiently learn from trial-and-error as traditional reinforcement learning methods require exte... | Noah Shinn; Federico Cassano; Edward Berman; Ashwin Gopinath; Karthik Narasimhan; Shunyu Yao | 2023 | 2023 | Advances in Neural Information Processing Systems 36 (NeurIPS) | Neural Information Processing Systems Foundation | 10.52202/075280-0377 | Published in Advances in Neural Information Processing Systems 36 (NeurIPS); the linked arXiv record remains available for open access. | cs.AI | NeurIPS proceedings and DOI records | 2303.11366 | 2026-08-31T13:57:28 | |||||||
ale-0133 | Research Foundations | research-foundations | Paper | 📄 | Self-Refine: Iterative Refinement with Self-Feedback | https://arxiv.org/abs/2303.17651 | external | arxiv.org | Generate-feedback-refine loop where a model improves outputs over repeated passes. | Generate-feedback-refine loop where a model improves outputs over repeated passes. | Generate-feedback-refine loop where a model improves outputs over repeated passes. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Generate-feedback-refine loop where a model improves outputs over repeated passes. | Use Self-Refine: Iterative Refinement with Self-Feedback to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2303.17651; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 711 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L711 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-paper | A | ok | https://proceedings.neurips.cc/paper_files/paper/2023/hash/91edff07232fb1b55a505a9e9f6c0ff3-Abstract-Conference.html | [2303.17651] Self-Refine: Iterative Refinement with Self-Feedback | Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement. The main idea is to generate an initial output ... | Aman Madaan; Niket Tandon; Prakhar Gupta; Skyler Hallinan; Luyu Gao; Sarah Wiegreffe; Uri Alon; Nouha Dziri; Shrimai Prabhumoye; Yiming Yang; Shashank Gupta; Bodhisattwa Prasad Majumder; Katherine Hermann; Sean Welleck; Amir Yazdanbakhsh; Peter Clark | 2023 | 2023 | Advances in Neural Information Processing Systems 36 (NeurIPS) | Neural Information Processing Systems Foundation | Published in Advances in Neural Information Processing Systems 36 (NeurIPS); the linked arXiv record remains available for open access. | cs.CL | NeurIPS proceedings record | 2303.17651 | 2026-08-31T13:57:28 | ||||||||
ale-0134 | Research Foundations | research-foundations | Paper | 📄 | CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing | https://arxiv.org/abs/2305.11738 | external | arxiv.org | Uses tools to ground critique and correction rather than relying only on introspection. | Uses tools to ground critique and correction rather than relying only on introspection. | Uses tools to ground critique and correction rather than relying only on introspection. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Uses tools to ground critique and correction rather than relying only on introspection. | Use CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2305.11738; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 712 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L712 | Learn | learn | Understand the field and its boundaries. | workspace;verification | researcher;evaluator | cross-layer | enabling | research-paper | A | ok | https://proceedings.iclr.cc/paper_files/paper/2024/hash/fef126561bbf9d4467dbb8d27334b8fe-Abstract-Conference.html | [2305.11738] CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing | Recent developments in large language models (LLMs) have been impressive. However, these models sometimes show inconsistencies and problematic behavior, such as hallucinating facts, generating flawed code, or creating offensive and toxic content. Unlike these models, humans typically utilize external tools to cross-che... | Zhibin Gou; Zhihong Shao; Yeyun Gong; Yelong Shen; Yujiu Yang; Nan Duan; Weizhu Chen | 2024 | 2024 | International Conference on Learning Representations (ICLR) | International Conference on Learning Representations | Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access. | cs.CL | ICLR proceedings record | 2305.11738 | 2026-08-31T13:57:28 | ||||||||
ale-0135 | Research Foundations | research-foundations | Paper | 📄 | Tree of Thoughts | https://arxiv.org/abs/2305.10601 | external | arxiv.org | Search over multiple reasoning branches; relevant when loop design needs exploration before committing. | Search over multiple reasoning branches; relevant when loop design needs exploration before committing. | Search over multiple reasoning branches; relevant when loop design needs exploration before committing. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Search over multiple reasoning branches; relevant when loop design needs exploration before committing. | Use Tree of Thoughts to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2305.10601; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 713 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L713 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-paper | A | ok | https://proceedings.neurips.cc/paper_files/paper/2023/hash/271db9922b8d1f4dd7aaef84ed5ac703-Abstract.html | [2305.10601] Tree of Thoughts: Deliberate Problem Solving with Large Language Models | Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions pla... | Shunyu Yao; Dian Yu; Jeffrey Zhao; Izhak Shafran; Thomas L. Griffiths; Yuan Cao; Karthik Narasimhan | 2023 | 2023 | Advances in Neural Information Processing Systems 36 (NeurIPS) | Neural Information Processing Systems Foundation | Published in Advances in Neural Information Processing Systems 36 (NeurIPS); the linked arXiv record remains available for open access. | cs.CL | NeurIPS proceedings record | 2305.10601 | 2026-08-31T13:57:28 | ||||||||
ale-0136 | Research Foundations | research-foundations | Paper | 📄 | Graph of Thoughts | https://arxiv.org/abs/2308.09687 | external | arxiv.org | Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation. | Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation. | Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation. | Control flow is represented as an inspectable graph rather than an opaque prompt loop. Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation. | Use Graph of Thoughts to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2308.09687; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 714 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L714 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-paper | A | ok | https://ojs.aaai.org/index.php/AAAI/article/view/29720 | [2308.09687] Graph of Thoughts: Solving Elaborate Problems with Large Language Models | We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is the ability to model the information generated by an LLM as an arbitra... | Maciej Besta; Nils Blach; Ales Kubicek; Robert Gerstenberger; Michal Podstawski; Lukas Gianinazzi; Joanna Gajda; Tomasz Lehmann; Hubert Niewiadomski; Piotr Nyczyk; Torsten Hoefler | 2024-03-24 | 2024 | Proceedings of the AAAI Conference on Artificial Intelligence 38 (AAAI) | Association for the Advancement of Artificial Intelligence (AAAI) | 10.1609/aaai.v38i16.29720 | Published in Proceedings of the AAAI Conference on Artificial Intelligence 38 (AAAI); the linked arXiv record remains available for open access. | cs.CL | AAAI proceedings and DOI records | 2308.09687 | 2026-08-31T13:57:28 | |||||||
ale-0137 | Research Foundations | research-foundations | Paper | 📄 | Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models | https://arxiv.org/abs/2310.04406 | external | arxiv.org | Combines search, action, and environment feedback for language agents. | Combines search, action, and environment feedback for language agents. | Combines search, action, and environment feedback for language agents. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Combines search, action, and environment feedback for language agents. | Use Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2310.04406; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 715 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L715 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-paper | A | ok | https://proceedings.mlr.press/v235/zhou24r.html | [2310.04406] Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models | While language models (LMs) have shown potential across a range of decision-making tasks, their reliance on simple acting processes limits their broad deployment as autonomous agents. In this paper, we introduce Language Agent Tree Search (LATS) -- the first general framework that synergizes the capabilities of LMs in ... | Andy Zhou; Kai Yan; Michal Shlapentokh-Rothman; Haohan Wang; Yu-Xiong Wang | 2024 | 2024 | Proceedings of the 41st International Conference on Machine Learning (ICML) | PMLR | Published in Proceedings of the 41st International Conference on Machine Learning (ICML); the linked arXiv record remains available for open access. | cs.AI | PMLR proceedings record | 2310.04406 | 2026-08-31T13:57:28 | ||||||||
ale-0138 | Research Foundations | research-foundations | Paper | 📄 | Voyager: An Open-Ended Embodied Agent with Large Language Models | https://arxiv.org/abs/2305.16291 | external | arxiv.org | Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library. | Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library. | Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library. | Use Voyager: An Open-Ended Embodied Agent with Large Language Models to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2305.16291; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 716 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L716 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-paper | A | ok | https://openreview.net/forum?id=ehfRiF0R3a | [2305.16291] Voyager: An Open-Ended Embodied Agent with Large Language Models | We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-gro... | Guanzhi Wang; Yuqi Xie; Yunfan Jiang; Ajay Mandlekar; Chaowei Xiao; Yuke Zhu; Linxi Fan; Anima Anandkumar | 2024 | 2024 | Transactions on Machine Learning Research (TMLR) | OpenReview | Published in Transactions on Machine Learning Research (TMLR); the linked arXiv record remains available for open access. | cs.AI | TMLR OpenReview record | 2305.16291 | 2026-08-31T13:57:28 | ||||||||
ale-0139 | Research Foundations | research-foundations | Paper | 📄 | Generative Agents: Interactive Simulacra of Human Behavior | https://arxiv.org/abs/2304.03442 | external | arxiv.org | Introduces reflection and memory mechanisms for long-running agent behavior. | Introduces reflection and memory mechanisms for long-running agent behavior. | Introduces reflection and memory mechanisms for long-running agent behavior. | Persistent memory is treated as an external runtime artifact. Introduces reflection and memory mechanisms for long-running agent behavior. | Use Generative Agents: Interactive Simulacra of Human Behavior to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2304.03442; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 717 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L717 | Learn | learn | Understand the field and its boundaries. | context;escalation | researcher;evaluator | cross-layer | enabling | research-paper | A | ok | https://doi.org/10.1145/3586183.3606763 | [2304.03442] Generative Agents: Interactive Simulacra of Human Behavior | Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents ... | Joon Sung Park; Joseph C. O'Brien; Carrie J. Cai; Meredith Ringel Morris; Percy Liang; Michael S. Bernstein | 2023-10-29 | 2023 | Proceedings of the 36th ACM Symposium on User Interface Software and Technology (UIST) | Association for Computing Machinery | 10.1145/3586183.3606763 | Published in Proceedings of the 36th ACM Symposium on User Interface Software and Technology (UIST); the linked arXiv record remains available for open access. | cs.HC | ACM DOI record | 2304.03442 | 2026-08-31T13:57:28 | |||||||
ale-0140 | Research Foundations | research-foundations | Paper | 📄 | Measuring AI Ability to Complete Long Software Tasks | https://arxiv.org/abs/2503.14499 | external | arxiv.org | METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer. | METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer. | METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer. | Checkpointed state makes long-running agent work recoverable across failures. METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer. | Use Measuring AI Ability to Complete Long Software Tasks to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2503.14499; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 718 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L718 | Learn | learn | Understand the field and its boundaries. | state;budget;escalation | researcher;evaluator | cross-layer | enabling | research-paper | A | ok | https://proceedings.neurips.cc/paper_files/paper/2025/hash/85069585133c4c168c865e65d72e9775-Abstract-Conference.html | [2503.14499] Measuring AI Ability to Complete Long Software Tasks | Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear. To quantify the capabilities of AI systems in terms of human capabilities, we propose a new metric: 50%-task-completion time horizon. This is the time humans typically take to complete tasks that AI models can comp... | Thomas Kwa; Ben West; Joel Becker; Amy Deng; Katharyn Garcia; Max Hasin; Sami Jawhar; Megan Kinniment; Nate Rush; Sydney Von Arx; Ryan Bloom; Thomas Broadley; Haoxing Du; Brian Goodrich; Nikola Jurkovic; Luke Harold Miles; Seraphina Nix; Tao Lin; Chris Painter; Neev Parikh; David Rein; Lucas Jun Koba Sato; Hjalmar Wijk... | 2025 | 2025 | Advances in Neural Information Processing Systems 38 (NeurIPS) | Neural Information Processing Systems Foundation | Published in Advances in Neural Information Processing Systems 38 (NeurIPS); the linked arXiv record remains available for open access. | cs.AI | NeurIPS proceedings record | 2503.14499 | 2026-08-31T13:57:28 | ||||||||
ale-0141 | Research Foundations | research-foundations | Blog | 📝 | Measuring AI Ability to Complete Long Tasks | https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/ | external | metr.org | Accessible summary of the 50% task-completion time horizon and its doubling trend. | Accessible summary of the 50% task-completion time horizon and its doubling trend. | Accessible summary of the 50% task-completion time horizon and its doubling trend. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Accessible summary of the 50% task-completion time horizon and its doubling trend. | Use Measuring AI Ability to Complete Long Tasks to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Contextual source from metr.org; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 719 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L719 | Learn | learn | Understand the field and its boundaries. | exit | builder | cross-layer | enabling | practitioner-analysis | B | ok | https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/ | Measuring AI Ability to Complete Long Software Tasks - METR Substack twitter Bluesky | We propose measuring AI performance in terms of the *length* of tasks AI agents can complete. We show that this metric has been consistently exponentially increasing over the past 6 years, with a doubling time of around 7 months. Extrapolating this trend predicts that, in under a decade, we will see AI agents that can ... | 2025-03-19 | 2025 | METR Blog | metr.org | html-meta | 2026-08-31T13:57:28 | ||||||||||||
ale-0142 | Research Foundations | research-foundations | Paper | 📄 | Reflection-Driven Control for Trustworthy Code Agents | https://arxiv.org/abs/2512.21354 | external | arxiv.org | Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead. | Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead. | Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead. | Use Reflection-Driven Control for Trustworthy Code Agents to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2512.21354; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 720 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L720 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-paper | A | ok | https://openreview.net/forum?id=vUtz66IHD1 | [2512.21354] Reflection-Driven Control for Trustworthy Code Agents | Contemporary large language model (LLM) agents are remarkably capable, but they still lack reliable safety controls and can produce unconstrained, unpredictable, and even actively harmful outputs. To address this, we introduce Reflection-Driven Control, a standardized and pluggable control module that can be seamlessly... | Bin Wang; Jiazheng Quan; Xingrui Yu; Hansen Hu; Yuhao; Ivor Tsang | 2026 | 2026 | AAAI Workshop on Trust and Control in Agentic AI (TrustAgent) | Association for the Advancement of Artificial Intelligence (AAAI) | Published in AAAI Workshop on Trust and Control in Agentic AI (TrustAgent); the linked arXiv record remains available for open access. | cs.CR | AAAI workshop OpenReview record | 2512.21354 | 2026-08-31T13:57:28 | ||||||||
ale-0143 | Research Foundations | research-foundations | Paper | 📄 | Hyperagents | https://arxiv.org/abs/2603.19461 | external | arxiv.org | Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs. | Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs. | Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs. | Use Hyperagents to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2603.19461; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 721 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L721 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2603.19461 | [2603.19461] Hyperagents | Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such systems can improve. The Darwin G\"odel Machine (D... | Jenny Zhang; Bingchen Zhao; Wannan Yang; Jakob Foerster; Jeff Clune; Minqi Jiang; Sam Devlin; Tatiana Shavrina | 2026-03-19 | 2026 | arXiv | arXiv | Code at https://github.com/facebookresearch/Hyperagents | cs.AI | arxiv-api | 2603.19461 | 2026-08-31T13:57:28 | ||||||||
ale-0144 | Research Foundations | research-foundations | Paper | 📄 | PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks | https://arxiv.org/abs/2512.03549 | external | arxiv.org | Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs. | Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs. | Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs. | The work targets tasks that exceed a single context window or prompt session. Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs. | Use PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2512.03549; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 722 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L722 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2512.03549 | [2512.03549] PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks | We introduce PARC, a coding agent for the autonomous and robust execution of long-horizon computational tasks. PARC is built on a hierarchical multi-agent architecture incorporating task planning, execution, and a mechanism that evaluates its own actions and their outcomes from an independent context and provides feedb... | Yuki Orimo; Iori Kurata; Hodaka Mori; Ryuhei Okuno; Ryohto Sawada; Daisuke Okanohara | 2025-12-03 | 2025 | arXiv | arXiv | cs.AI | arxiv-api | 2512.03549 | 2026-08-31T13:57:28 | |||||||||
ale-0145 | Research Foundations | research-foundations | Paper | 📄 | When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents | https://arxiv.org/abs/2603.17104 | external | arxiv.org | Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss. | Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss. | Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss. | The work targets tasks that exceed a single context window or prompt session. Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss. | Use When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2603.17104; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 723 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L723 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2603.17104 | [2603.17104] When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents | Current coding-agent benchmarks usually pro- vide the full task specification upfront. Real research coding often does not: the intended system is progressively disclosed through in- teraction, requiring the agent to track durable design commitments across a long session. We introduce a benchmark for this setting and s... | Lu Yan; Xuan Chen; Xiangyu Zhang | 2026-03-17 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2603.17104 | 2026-08-31T13:57:28 | |||||||||
ale-0146 | Research Foundations | research-foundations | Tool | 🧰 | Reflexion code | https://github.com/noahshinn/reflexion | external | github.com | Reference implementation and experiments for verbal reinforcement loops. | Reference implementation and experiments for verbal reinforcement loops. | Reference implementation and experiments for verbal reinforcement loops. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Reference implementation and experiments for verbal reinforcement loops. | Use Reflexion code to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 724 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L724 | Learn | learn | Understand the field and its boundaries. | whole-loop | builder | cross-layer | enabling | source-implementation | A | ok | https://github.com/noahshinn/reflexion | GitHub - noahshinn/reflexion: [NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning · GitHub | [NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning - noahshinn/reflexion | noahshinn/reflexion | GitHub | html-meta | noahshinn/reflexion | 2026-08-31T13:57:28 | |||||||||||||
ale-0147 | Research Foundations | research-foundations | Paper | 📄 | Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting | https://arxiv.org/abs/2607.00038 | external | arxiv.org | Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops. | Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops. | Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops. | Verification is promoted from a final check to a loop-control signal. Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty r... | Use Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.00038; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 725 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L725 | Learn | learn | Understand the field and its boundaries. | objective;trigger;context;verification;exit | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.00038 | [2607.00038] Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting | In mid-2026 a slogan reorganized how practitioners talk about coding agents: stop prompting your agent, start designing the loop that prompts it. We take this claim seriously and give it a careful treatment. We call the object of the new practice the loop specification: a bounded, reusable artifact, made of a trigger, ... | Sandeco Macedo | 2026-06-28 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2607.00038 | 2026-08-31T13:57:28 | |||||||||
ale-0148 | Research Foundations | research-foundations | Paper | 📄 | From Question Answering to Task Completion: A Survey on Agent System and Harness Design | https://arxiv.org/abs/2606.20683 | external | arxiv.org | Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone. | Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone. | Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone. | Verification is promoted from a final check to a loop-control signal. Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluati... | Use From Question Answering to Task Completion: A Survey on Agent System and Harness Design to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2606.20683; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 726 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L726 | Learn | learn | Understand the field and its boundaries. | context;verification;state;exit | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2606.20683 | [2606.20683] From Question Answering to Task Completion: A Survey on Agent System and Harness Design | LLM-based agents mark a shift from passive question answering to active task completion: they perceive environments, invoke tools, maintain state, and act over extended horizons. As agent systems have evolved from prompt engineering to workflows and context engineering, harness engineering, and agent-native training wi... | Jianyuan Guo; Zhiwei Hao; Chengcheng Wang; Cheng Fan; Tingzhang Luo; Hongguang Li; Ying Gao; Hefei Mei; Jiankun Peng; Rongjian Xu; Minjing Dong; Han Wu; Mengyu Zheng; Kai Han; Shiqi Wang; Chang Xu; Yunhe Wang | 2026-06-14 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2606.20683 | 2026-08-31T13:57:28 | |||||||||
ale-0149 | Research Foundations | research-foundations | Paper | 📄 | MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems | https://arxiv.org/abs/2605.22794 | external | arxiv.org | Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention. | Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention. | Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention. | Durable execution and replay are treated as first-class loop infrastructure. Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.6... | Use MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2605.22794; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 727 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L727 | Learn | learn | Understand the field and its boundaries. | verification;state;escalation | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2605.22794 | [2605.22794] MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems | Autonomous agentic systems are largely static after deployment: they do not learn from user interactions, and recurring failures persist until the next human-driven update ships a fix. Self-evolving agents have emerged in response, but all confine evolution to text-mutable artifacts -- skill files, prompt configuration... | Qianshu Cai; Yonggang Zhang; Xianzhang Jia; Huajiang Zheng; Wei Xue; Jun Song; Xinmei Tian; Yike Guo | 2026-05-21 | 2026 | arXiv | arXiv | 12 pages, 3 figures, 2 tables. Preprint. Code: https://github.com/hkgai-official/Moss | cs.AI | arxiv-api | 2605.22794 | 2026-08-31T13:57:28 | ||||||||
ale-0150 | Research Foundations | research-foundations | Blog | 📝 | METR Time Horizon 1.1 | https://metr.org/blog/2026-1-29-time-horizon-1-1/ | external | metr.org | Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days. | Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days. | Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days. | Use METR Time Horizon 1.1 to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Contextual source from metr.org; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 728 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L728 | Learn | learn | Understand the field and its boundaries. | whole-loop | builder | cross-layer | enabling | practitioner-analysis | B | ok | https://metr.org/blog/2026-1-29-time-horizon-1-1/ | Time Horizon 1.1 - METR Substack twitter Bluesky | We’re releasing a new version of our time horizon estimates (TH1.1), using more tasks and a new eval infrastructure. | 2026-01-29 | 2026 | METR Blog | metr.org | html-meta | 2026-08-31T13:57:28 | ||||||||||||
ale-0151 | Research Foundations | research-foundations | Paper | 📄 | MetaSkill-Evolve: Recursive Self-Improvement via Two-Timescale Meta-Skill Evolution | https://arxiv.org/abs/2607.05297 | external | arxiv.org | Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld. | Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld. | Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld. | Use MetaSkill-Evolve: Recursive Self-Improvement via Two-Timescale Meta-Skill Evolution to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.05297; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 729 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L729 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.05297 | [2607.05297] MetaSkill-Evolve: Recursive Self-Improvement of LLM Agents via Two-Timescale Meta-Skill Evolution | Recent LLM agents tackle increasingly long-horizon, open-ended tasks, and external skills, reusable procedural knowledge supplied to the agent, further extend this capability. However, a fixed, hand-authored skill is rarely optimal, and cannot adapt to the diversity of tasks an agent encounters. Self-improving agents a... | Zefeng Wang; Minxi Yan; Jinhe Bi; Sikuan Yan; Volker Tresp; Yunpu Ma | 2026-07-06 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.05297 | 2026-08-31T13:57:28 | |||||||||
ale-0152 | Research Foundations | research-foundations | Paper | 📄 | SkillOpt-Lite: Better and Faster Agent Self-Evolution via One Line of Vibe | https://arxiv.org/abs/2607.03451 | external | arxiv.org | Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench. | Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench. | Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass large... | Use SkillOpt-Lite: Better and Faster Agent Self-Evolution via One Line of Vibe to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.03451; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 730 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L730 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.03451 | [2607.03451] SkillOpt-Lite: Better and Faster Agent Self-evolution via One Line of Vibe | While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines. This leaves a fundamental question unaddressed: What constitutes a minimal viable pipeline for skill optimization, where every component is justified by theory or empirical necessity? We formalize skill optim... | Yifei Shen; Bo Li; Xinjie Zhang | 2026-07-03 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2607.03451 | 2026-08-31T13:57:28 | |||||||||
ale-0153 | Research Foundations | research-foundations | Paper | 📄 | Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops | https://arxiv.org/abs/2607.07663 | external | arxiv.org | Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement. | Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement. | Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement. | Use Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.07663; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 731 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L731 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.07663 | [2607.07663] Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops | AI systems increasingly participate in their own improvement: revising their outputs, adapting their own harnesses during deployment, training on data they generate, and, increasingly, conducting AI research itself. This literature is described under a vocabulary ("self-refine," "self-reward," "self-play," "self-evolve... | Mingguang Chen; Licheng Wang; Bo Qu | 2026-07-08 | 2026 | arXiv | arXiv | 42 pages, 6 figures | cs.AI | arxiv-api | 2607.07663 | 2026-08-31T13:57:28 | ||||||||
ale-0154 | Research Foundations | research-foundations | Paper | 📄 | From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents | https://arxiv.org/abs/2607.07321 | external | arxiv.org | EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle. | EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle. | EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle. | Evaluation data is used as the feedback signal for improving loop behavior. EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle. | Use From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.07321; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 732 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L732 | Learn | learn | Understand the field and its boundaries. | workspace;verification | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.07321 | [2607.07321] From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents | Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks predominantly rely on static toolsets composed of granular atomic actions (e.g., basic file I/O or single-turn search), which forces agents to reinvent low-level logic... | Haipeng Ding; Yuexiang Xie; Zhewei Wei; Yaliang Li; Bolin Ding | 2026-07-08 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.07321 | 2026-08-31T13:57:28 | |||||||||
ale-0155 | Research Foundations | research-foundations | Paper | 📄 | TTHE: Test-Time Harness Evolution | https://arxiv.org/abs/2607.08124 | external | arxiv.org | Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as t... | Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as t... | Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as t... | Connects Loop Engineering to prior agent-loop and feedback-loop research. Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and compe... | Use TTHE: Test-Time Harness Evolution to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.08124; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 733 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L733 | Learn | learn | Understand the field and its boundaries. | verification | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.08124 | [2607.08124] TTHE: Test-Time Harness Evolution | The behavior of an LLM agent is determined not only by the underlying model, but also by its harness: the executable program that constructs context, invokes tools, verifies intermediate results, and recovers from failures. Existing approaches optimize such harnesses before deployment, searching training or development... | Jun Nie; Yonggang Zhang; Jun Song; Qianshu Cai; Dahai Yu; Yike Guo; Xinmei Tian; Bo Han | 2026-07-09 | 2026 | arXiv | arXiv | 15 pages, 5 figures | cs.SE | arxiv-api | 2607.08124 | 2026-08-31T13:57:28 | ||||||||
ale-0156 | Research Foundations | research-foundations | Paper | 📄 | DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment | https://arxiv.org/abs/2607.07820 | external | arxiv.org | Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and... | Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and... | Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and... | Verification is promoted from a final check to a loop-control signal. Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher traject... | Use DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.07820; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 734 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L734 | Learn | learn | Understand the field and its boundaries. | verification | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.07820 | [2607.07820] DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment | Training tool-use agents to improve from their own experience remains challenging, as supervised fine-tuning relies on fixed teacher-distilled trajectories, while sparse-reward reinforcement learning provides weak supervision for long-horizon interactions. We present DeepSearch-Evolve, a self-distillation framework for... | Xinyu Geng; Xuanhua He; Sixiang Chen; Yanjing Xiao; Fan Zhang; Shijue Huang; Haitao Mi; Zhenwen Liang; Tianqing Fang; Yi R. Fung | 2026-07-08 | 2026 | arXiv | arXiv | cs.CL | arxiv-api | 2607.07820 | 2026-08-31T13:57:28 | |||||||||
ale-0157 | Research Foundations | research-foundations | Paper | 📄 | What Makes a Good Bug Report for an AI Agent? | https://arxiv.org/abs/2607.07593 | external | arxiv.org | Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery ... | Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery ... | Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery ... | Connects Loop Engineering to prior agent-loop and feedback-loop research. Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports cor... | Use What Makes a Good Bug Report for an AI Agent? to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.07593; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 735 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L735 | Learn | learn | Understand the field and its boundaries. | intake | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.07593 | [2607.07593] What Makes a Good Bug Report for an AI Agent? | Automated program repair (APR) agents are transitioning from research benchmarks to developer workflows, yet they still begin with bug reports written for human developers. While decades of research have established what makes a good bug report for humans (e.g., steps to reproduce, stack traces), it remains unclear whe... | Lara Khatib; Noble Saji Mathews; Meiyappan Nagappan; Pengyu Nie; Thomas Zimmermann | 2026-07-08 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2607.07593 | 2026-08-31T13:57:28 | |||||||||
ale-0158 | Research Foundations | research-foundations | Paper | 📄 | AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution | https://arxiv.org/abs/2607.08252 | external | arxiv.org | Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-gove... | Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-gove... | Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-gove... | State persistence is explicit enough for repeated runs and handoff. Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-time... | Use AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.08252; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 736 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L736 | Learn | learn | Understand the field and its boundaries. | verification;state | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.08252 | [2607.08252] AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution | Long-term persona agents must remain identifiable while adapting to new events, relationships, evidence, and social conditions. We identify self-locking as a runtime failure mode in continuing persona-life loops: locally plausible events keep appearing while the generated life collapses toward familiar environments, we... | Mengchen Li | 2026-07-09 | 2026 | arXiv | arXiv | 52 pages, 13 figures/tables, ancillary public-safe evaluation artifacts included | cs.AI | arxiv-api | 2607.08252 | 2026-08-31T13:57:28 | ||||||||
ale-0159 | Research Foundations | research-foundations | Paper | 📄 | Agentic Data Environments | https://arxiv.org/abs/2607.07397 | external | arxiv.org | Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cos... | Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cos... | Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cos... | State persistence is explicit enough for repeated runs and handoff. Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both ampli... | Use Agentic Data Environments to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.07397; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 737 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L737 | Learn | learn | Understand the field and its boundaries. | state;budget | researcher;evaluator | cross-layer | enabling | research-paper | A | ok | http://sites.computer.org/debull/A26mar/A26MAR-CD.pdf#page=7 | [2607.07397] Agentic Data Environments | Autonomous agents promise substantial gains in speed, scale, and labor efficiency, but their failures can impose abrupt and often irreversible costs. The central challenge for agentic automation is therefore to increase the benefits of automation while bounding the consequences of failure. While databases remain centra... | Elaine Ang; Chenxi Huang; Georgios Liargkovas; Jerry Liu; Jinhui Liu; Nikos Pagonas; Charlie Summers; Haonan Wang; Jiakai Xu; Tianle Zhou; Yusen Zhang; Zhou Yu; Zhuo Zhang; Tianyi Peng; Kostis Kaffes; Eugene Wu | 2026-03 | 2026 | IEEE Data Engineering Bulletin 50(1) | IEEE | Published in IEEE Data Engineering Bulletin 50(1); the linked arXiv record remains available for open access. | cs.AI | IEEE Data Engineering Bulletin record | 2607.07397 | 2026-08-31T13:57:28 | ||||||||
ale-0160 | Research Foundations | research-foundations | Paper | 📄 | Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation | https://arxiv.org/abs/2607.08938 | external | arxiv.org | Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target. | Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target. | Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target. | Use Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.08938; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 738 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L738 | Learn | learn | Understand the field and its boundaries. | budget | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.08938 | [2607.08938] Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation | Frontier LLM agents are automating many business tasks, but their high inference cost makes large-scale deployment unsustainable. Small language models (SLMs) offer a cheaper alternative, yet they typically fall short when swapped into a harness designed for a frontier LLM. We show that for many routine business tasks,... | Chenyang Yang; Xinran Zhao; Tongshuang Wu; Christian Kästner | 2026-07-09 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2607.08938 | 2026-08-31T13:57:28 | |||||||||
ale-0161 | Research Foundations | research-foundations | Paper | 📄 | Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills | https://arxiv.org/abs/2607.09065 | external | arxiv.org | First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that age... | First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that age... | First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that age... | Connects Loop Engineering to prior agent-loop and feedback-loop research. First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how... | Use Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.09065; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 739 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L739 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.09065 | [2607.09065] Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills | Software engineering (abbrev. SE) has continuously evolved through increasingly powerful forms of reuse, from source code and libraries to components and services. Recent advances in AI agents have introduced a potentially new reusable artifact: skills. Emerging agent skill repositories and marketplaces enable develope... | Jialun Cao; Xinru Yan; Songqiang Chen; Yaojie Lu; Zhongxin Liu; Shing-Chi Cheung | 2026-07-10 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2607.09065 | 2026-08-31T13:57:28 | |||||||||
ale-0162 | Research Foundations | research-foundations | Blog | 📝 | Harness Engineering for Self-Improvement | https://lilianweng.github.io/posts/2026-07-04-harness/ | external | lilianweng.github.io | Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes. | Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes. | Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes. | Orchestration and control flow are made explicit and inspectable. Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes. | Use Harness Engineering for Self-Improvement to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Contextual source from lilianweng.github.io; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 740 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L740 | Learn | learn | Understand the field and its boundaries. | delegation | builder | cross-layer | enabling | practitioner-analysis | B | ok | https://lilianweng.github.io/posts/2026-07-04-harness/ | Harness Engineering for Self-Improvement | Lil'Log | The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965), where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. Yudkowsky (2008) used the phrase “recursive self-improvement” for a specific fe... | Lilian Weng | 2026-07-04 | 2026 | lilianweng.github.io | html-meta | 2026-08-31T13:57:28 | ||||||||||||
ale-0163 | Research Foundations | research-foundations | Paper | 📄 | Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime | https://arxiv.org/abs/2607.11346 | external | arxiv.org | Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench ... | Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench ... | Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench ... | Connects Loop Engineering to prior agent-loop and feedback-loop research. Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps str... | Use Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.11346; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 741 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L741 | 2026-07-15 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.11346 | [2607.11346] Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime for Procedural LLM Agents | Enterprise agents must follow long-horizon, conditional, safety-critical standard operating procedures (SOPs). We compile machine-readable SOP constraints into executable pseudo-code and run them with a program-guided (PG) stack machine that pages the active frame while an LLM performs semantic execution. A three-arm S... | Chenglin Yu; Li Yin; Qingxin Fan; Ying Yu; RunyangRay Zhong; Ming Li | 2026-07-13 | 2026 | arXiv | arXiv | 9 pages, 3 figures, 5 tables | cs.AI | arxiv-api | 2607.11346 | 2026-08-31T13:57:28 | |||||||
ale-0164 | Research Foundations | research-foundations | Paper | 📄 | Mako: A Self-Evolving Agentic Operating System for Autonomous Web Exploitation | https://arxiv.org/abs/2607.11288 | external | arxiv.org | Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification. | Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification. | Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification. | Verification is promoted from a final check to a loop-control signal. Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop ... | Use Mako: A Self-Evolving Agentic Operating System for Autonomous Web Exploitation to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.11288; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 742 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L742 | 2026-07-15 | Learn | learn | Understand the field and its boundaries. | verification | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.11288 | [2607.11288] Mako: A Self-Evolving Agentic Operating System (SE-AOS) for Autonomous Web Exploitation | We introduce the Self-Evolving Agentic Operating System (SE-AOS): a new class of AI agent that treats exploit capability as a mutable, versioned kernel it extends at runtime, observing its own failures, synthesising new capabilities, proving them against a live target, and hot-loading them back into itself. Mako is the... | Praneeth Narisetty; Shiva Nagendra Babu Kore | 2026-07-13 | 2026 | arXiv | arXiv | 13 pages, 10 figures, 8 tables | cs.CR | arxiv-api | 2607.11288 | 2026-08-31T13:57:28 | |||||||
ale-0165 | Research Foundations | research-foundations | Paper | 📄 | How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study | https://arxiv.org/abs/2607.10856 | external | arxiv.org | Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions. | Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions. | Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions. | Verification is promoted from a final check to a loop-control signal. Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions. | Use How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.10856; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 743 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L743 | 2026-07-15 | Learn | learn | Understand the field and its boundaries. | verification | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.10856 | [2607.10856] How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study | The rise of Software Engineering (SE) agents, i.e., LLM-based agents that can understand large codebases and carry out engineering tasks with limited human intervention, has been marked by rapid advances and adoption, but little is known about how developers build these systems in practice: existing studies mine reposi... | Yunbo Lyu; David Williams; Jieke Shi; Zhensu Sun; Chao Peng; Zhou Yang; Federica Sarro; David Lo | 2026-07-12 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2607.10856 | 2026-08-31T13:57:28 | ||||||||
ale-0166 | Research Foundations | research-foundations | Paper | 📄 | Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries | https://arxiv.org/abs/2607.10113 | external | arxiv.org | Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework. | Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework. | Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework. | Use Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.10113; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 744 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L744 | 2026-07-15 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-paper | A | ok | https://openreview.net/forum?id=cjU3YbcRr8 | [2607.10113] Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries | Large language model agents increasingly store reusable procedures outside the model. These reusable procedures are often called \emph{skills}: they may be code functions, natural-language instructions, SKILL.md packages, workflow graphs, or learned adapters that a future agent can retrieve and invoke. This taxonomy-dr... | Yubo Li | 2026 | 2026 | Transactions on Machine Learning Research (TMLR) | OpenReview | Accepted at Transactions on Machine Learning Research (TMLR); the linked arXiv record is the available paper version. | cs.AI | Current arXiv acceptance note and OpenReview record | 2607.10113 | 2026-08-31T13:57:28 | |||||||
ale-0167 | Research Foundations | research-foundations | Paper | 📄 | SIA: Self Improving AI with Harness & Weight Updates | https://arxiv.org/abs/2605.27276 | external | arxiv.org | Co-evolves a task agent's harness and model weights through a meta-agent, target agent, and feedback agent that evaluate outcomes and carry improvements across generations. | Co-evolves a task agent's harness and model weights through a meta-agent, target agent, and feedback agent that evaluate outcomes and carry improvements across generations. | Co-evolves a task agent's harness and model weights through a meta-agent, target agent, and feedback agent that evaluate outcomes and carry improvements across generations. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Co-evolves a task agent's harness and model weights through a meta-agent, target agent, and feedback agent that evaluate outcomes and carry improvements across generations. | Use SIA: Self Improving AI with Harness & Weight Updates to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2605.27276; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 748 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L748 | 2026-07-18 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2605.27276 | [2605.27276] SIA: Self Improving AI with Harness & Weight Updates | Humans are the bottleneck in building and improving AI. Both the models and the agents that wrap them are written, tuned, and corrected by people. The long-horizon goal of an AI that can figure out how to improve itself remains open. Two largely disjoint research lines attack this bottleneck. The harness-update school ... | Prannay Hebbar; Yogendra Manawat; Samuel Verboomen; Alesia Ivanova; Selvam Palanimalai; Kunal Bhatia; Vignesh Baskaran | 2026-05-26 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2605.27276 | 2026-08-31T13:57:28 | ||||||||
ale-0168 | Research Foundations | research-foundations | Paper | 📄 | Self-Improvements in Modern Agentic Systems: A Survey | https://arxiv.org/abs/2607.13104 | external | arxiv.org | Survey from the Zhuge/Schmidhuber group that frames a modern agent as a foundation model coupled to an operational scaffold (prompts, memory, tools, control logic) and organizes self-improvement research by which scaffold component gets updated and what signal drives the update. | Survey from the Zhuge/Schmidhuber group that frames a modern agent as a foundation model coupled to an operational scaffold (prompts, memory, tools, control logic) and organizes self-improvement research by which scaffold component gets updated and what signal drives the update. | Survey from the Zhuge/Schmidhuber group that frames a modern agent as a foundation model coupled to an operational scaffold (prompts, memory, tools, control logic) and organizes self-improvement research by which scaffold component gets updated and what signal drives the update. | Persistent memory is treated as an external runtime artifact. Survey from the Zhuge/Schmidhuber group that frames a modern agent as a foundation model coupled to an operational scaffold (prompts, memory, tools, control logic) and organizes self-improvement research by which scaffold component gets updated and what sign... | Use Self-Improvements in Modern Agentic Systems: A Survey to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.13104; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 749 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L749 | 2026-07-22 | Learn | learn | Understand the field and its boundaries. | workspace;context | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.13104 | [2607.13104] Self-Improvements in Modern Agentic Systems: A Survey | Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or even no human input. This survey frames modern self-improving agents as adaptive systems that convert experience into accumulated capabilit... | Zhe Ren; Yimeng Chen; Dandan Guo; Guowei Rong; Tonghui Li; R. B. Xiong; Qingfeng Lan; Wenyi Wang; Li Nanbo; Yibo Yang; Mingchen Zhuge; Jürgen Schmidhuber | 2026-07-14 | 2026 | arXiv | arXiv | 97 pages, 12 figures. Project page: https://selfimproving-agent.github.io/ Repository: https://github.com/selfimproving-agent/awesome-Self-Improving-Agents | cs.AI | arxiv-api | 2607.13104 | 2026-08-31T13:57:28 | |||||||
ale-0169 | Research Foundations | research-foundations | Paper | 📄 | Self-Evolving Agent Harnesses via Gated Semantic Quality-Diversity | https://arxiv.org/abs/2607.13683 | external | arxiv.org | Evolves the agent harness (prompts, injected knowledge, runtime config) with model weights frozen: an LLM diagnoses failures and proposes patches while deterministic code owns all sampling and significance testing, and accepted patches populate a pathology-keyed quality-diversity archive, with sealed-test gains of 9-15... | Evolves the agent harness (prompts, injected knowledge, runtime config) with model weights frozen: an LLM diagnoses failures and proposes patches while deterministic code owns all sampling and significance testing, and accepted patches populate a pathology-keyed quality-diversity archive, with sealed-test gains of 9-15... | Evolves the agent harness (prompts, injected knowledge, runtime config) with model weights frozen: an LLM diagnoses failures and proposes patches while deterministic code owns all sampling and significance testing, and accepted patches populate a pathology-keyed quality-diversity archive, with sealed-test gains of 9-15... | Connects Loop Engineering to prior agent-loop and feedback-loop research. Evolves the agent harness (prompts, injected knowledge, runtime config) with model weights frozen: an LLM diagnoses failures and proposes patches while deterministic code owns all sampling and significance testing, and accepted patches populate a... | Use Self-Evolving Agent Harnesses via Gated Semantic Quality-Diversity to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.13683; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 750 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L750 | 2026-07-22 | Learn | learn | Understand the field and its boundaries. | verification | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.13683 | [2607.13683] HarnessBank: Semantic Gene-Bank Search with Gated Verification for Agent-Harness Self-Evolution | Large Language Models (LLMs) have enabled capable agents across diverse applications. Beyond the foundation model, the performance of an agent is governed by the surrounding agent harness, including prompts, tools, control loops, etc. Automatically evolving this harness offers a promising pathway to agent improvement, ... | Xiaotian Luo; Dizhan Xue; Fengxingyu Wang; Chuanrui Hu; Yafeng Deng | 2026-07-15 | 2026 | arXiv | arXiv | 9 pages, 4 figures, 3 tables | cs.CL | arxiv-api | 2607.13683 | 2026-08-31T13:57:28 | |||||||
ale-0170 | Research Foundations | research-foundations | Paper | 📄 | XScientist: A Git-Like Research Protocol for Long-Running Autonomous Scientific Discovery | https://arxiv.org/abs/2607.12301 | external | arxiv.org | Treats long-running autonomous research as a continuous observable pipeline: exploration recorded as portable checkpointed artifacts with code, outputs, and evidence links, plus repair loops and human-in-the-loop quality gates, so failed branches stay inspectable. Single-author work, content is on-target for long-horiz... | Treats long-running autonomous research as a continuous observable pipeline: exploration recorded as portable checkpointed artifacts with code, outputs, and evidence links, plus repair loops and human-in-the-loop quality gates, so failed branches stay inspectable. Single-author work, content is on-target for long-horiz... | Treats long-running autonomous research as a continuous observable pipeline: exploration recorded as portable checkpointed artifacts with code, outputs, and evidence links, plus repair loops and human-in-the-loop quality gates, so failed branches stay inspectable. Single-author work, content is on-target for long-horiz... | Checkpointed state makes long-running agent work recoverable across failures. Treats long-running autonomous research as a continuous observable pipeline: exploration recorded as portable checkpointed artifacts with code, outputs, and evidence links, plus repair loops and human-in-the-loop quality gates, so failed bran... | Use XScientist: A Git-Like Research Protocol for Long-Running Autonomous Scientific Discovery to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.12301; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 751 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L751 | 2026-07-22 | Learn | learn | Understand the field and its boundaries. | intake;state;escalation | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.12301 | [2607.12301] XScientist: A Git-Like Research Protocol for Long-Running Autonomous Scientific Discovery | Autonomous research systems are often evaluated as one-shot paper generators: given a topic, they produce a manuscript and a small set of experiment logs. This framing hides the operational problem that makes such systems difficult to trust: research is long-running, branching, failure-prone, and dependent on auditable... | Jixiang Luo | 2026-07-14 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2607.12301 | 2026-08-31T13:57:28 | ||||||||
ale-0171 | Research Foundations | research-foundations | Paper | 📄 | Knowledge-Centric Self-Improvement | https://arxiv.org/abs/2607.19592 | external | arxiv.org | Proposes a knowledge-centric alternative to agent-centric self-improvement: agents stay generic and disposable while the persistent, improving object is a curated shared knowledge base that agents write evidence-grounded insights into and then distill, reporting higher solve rates at lower cost with knowledge that tran... | Proposes a knowledge-centric alternative to agent-centric self-improvement: agents stay generic and disposable while the persistent, improving object is a curated shared knowledge base that agents write evidence-grounded insights into and then distill, reporting higher solve rates at lower cost with knowledge that tran... | Proposes a knowledge-centric alternative to agent-centric self-improvement: agents stay generic and disposable while the persistent, improving object is a curated shared knowledge base that agents write evidence-grounded insights into and then distill, reporting higher solve rates at lower cost with knowledge that tran... | State persistence is explicit enough for repeated runs and handoff. Proposes a knowledge-centric alternative to agent-centric self-improvement: agents stay generic and disposable while the persistent, improving object is a curated shared knowledge base that agents write evidence-grounded insights into and then distill,... | Use Knowledge-Centric Self-Improvement to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.19592; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 752 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L752 | 2026-07-23 | Learn | learn | Understand the field and its boundaries. | state;budget | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.19592 | [2607.19592] Knowledge-Centric Self-Improvement | Self-improving AI systems typically treat the agent as the object that improves, by optimizing prompts, workflows, harnesses, or even the agent's own code. This agent-centric view can make improvements expensive to maintain and difficult to transfer, because gains become tied to a particular agent design, task distribu... | Xuefei Julie Wang; Lauren Hyoseo Yoon; Chengrui Qu; Amanda Zichang Wang; Atharva Sehgal; Eric Mazumdar; Yisong Yue | 2026-07-21 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.19592 | 2026-08-31T13:57:28 | ||||||||
ale-0172 | Research Foundations | research-foundations | Paper | 📄 | OpenForgeRL: Train Harness-native Agents in Any Environment | https://arxiv.org/abs/2607.21557 | external | arxiv.org | Open-source framework for end-to-end SFT/RL training of agents that run inside real inference harnesses (Claude Code, OpenClaw) via a lightweight proxy that serves the harness's model calls while recording them as training data, with Kubernetes-orchestrated distributed rollouts. | Open-source framework for end-to-end SFT/RL training of agents that run inside real inference harnesses (Claude Code, OpenClaw) via a lightweight proxy that serves the harness's model calls while recording them as training data, with Kubernetes-orchestrated distributed rollouts. | Open-source framework for end-to-end SFT/RL training of agents that run inside real inference harnesses (Claude Code, OpenClaw) via a lightweight proxy that serves the harness's model calls while recording them as training data, with Kubernetes-orchestrated distributed rollouts. | Orchestration and control flow are made explicit and inspectable. Open-source framework for end-to-end SFT/RL training of agents that run inside real inference harnesses (Claude Code, OpenClaw) via a lightweight proxy that serves the harness's model calls while recording them as training data, with Kubernetes-orchestra... | Use OpenForgeRL: Train Harness-native Agents in Any Environment to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.21557; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 753 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L753 | 2026-07-24 | Learn | learn | Understand the field and its boundaries. | delegation | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.21557 | [2607.21557] OpenForgeRL: Train Harness-native Agents in Any Environment | Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems. While powerful, these complex harnesses also make agents hard to train end-to-end with open infrastructure, whose SFT/RL stacks cannot natively express... | Xiao Yu; Baolin Peng; Ruize Xu; Hao Zou; Qianhui Wu; Hao Cheng; Wenlin Yao; Nikhil Singh; Zhou Yu; Jianfeng Gao | 2026-07-23 | 2026 | arXiv | arXiv | added github link | cs.AI | arxiv-api | 2607.21557 | 2026-08-31T13:57:28 | |||||||
ale-0173 | Research Foundations | research-foundations | Paper | 📄 | AREX: Towards a Recursively Self-Improving Agent for Deep Research | https://arxiv.org/abs/2607.21461 | external | arxiv.org | Family of recursively self-improving deep-research agents built on the discovery-verification asymmetry: an inner research loop gathers evidence while an outer loop audits the answer constraint-wise, flags unresolved claims, and launches targeted follow-up research, with autonomously learned compression of interaction ... | Family of recursively self-improving deep-research agents built on the discovery-verification asymmetry: an inner research loop gathers evidence while an outer loop audits the answer constraint-wise, flags unresolved claims, and launches targeted follow-up research, with autonomously learned compression of interaction ... | Family of recursively self-improving deep-research agents built on the discovery-verification asymmetry: an inner research loop gathers evidence while an outer loop audits the answer constraint-wise, flags unresolved claims, and launches targeted follow-up research, with autonomously learned compression of interaction ... | Verification is promoted from a final check to a loop-control signal. Family of recursively self-improving deep-research agents built on the discovery-verification asymmetry: an inner research loop gathers evidence while an outer loop audits the answer constraint-wise, flags unresolved claims, and launches targeted fol... | Use AREX: Towards a Recursively Self-Improving Agent for Deep Research to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.21461; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 754 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L754 | 2026-07-24 | Learn | learn | Understand the field and its boundaries. | intake;verification | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.21461 | [2607.21461] AREX: Towards a Recursively Self-Improving Agent for Deep Research | Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candidate can often be decomposed into tractable constraint-wise checks. This discovery--verification asymmetry suggests that a research agent should do more than simply searc... | Shuqi Lu; Chaofan Li; Kun Luo; Zhang Zhang; Hui Wang; Hongwang Xiao; Lei Xiong; Jiahao Wang; Sen Wang; Xiyan Jiang; Wanli Li; Yuyang Hu; Hongjin Qian; Bingyu Yan; Jianlyu Chen; Ziyi Xia; Yingxia Shao; Kang Liu; Zhicheng Dou; Di He; Chaozhuo Li; Qiwei Ye; Zhongyuan Wang; Zheng Liu | 2026-07-23 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.21461 | 2026-08-31T13:57:28 | ||||||||
ale-0174 | Research Foundations | research-foundations | Paper | 📄 | Workflow-Localized Mechanism Learning: Attribution-Guided Repair and Knowledge Reuse for Structured Agent Skills | https://arxiv.org/abs/2607.20999 | external | arxiv.org | Optimizes Agent Skills (reusable procedural knowledge for frozen-model agents) by jointly resolving where a workflow failed, which mechanism caused it, and which third-party Skill knowledge to reuse locally: node-mechanism attribution pinpoints the failed node and smallest valid edit target, bounded patches apply the r... | Optimizes Agent Skills (reusable procedural knowledge for frozen-model agents) by jointly resolving where a workflow failed, which mechanism caused it, and which third-party Skill knowledge to reuse locally: node-mechanism attribution pinpoints the failed node and smallest valid edit target, bounded patches apply the r... | Optimizes Agent Skills (reusable procedural knowledge for frozen-model agents) by jointly resolving where a workflow failed, which mechanism caused it, and which third-party Skill knowledge to reuse locally: node-mechanism attribution pinpoints the failed node and smallest valid edit target, bounded patches apply the r... | Connects Loop Engineering to prior agent-loop and feedback-loop research. Optimizes Agent Skills (reusable procedural knowledge for frozen-model agents) by jointly resolving where a workflow failed, which mechanism caused it, and which third-party Skill knowledge to reuse locally: node-mechanism attribution pinpoints t... | Use Workflow-Localized Mechanism Learning: Attribution-Guided Repair and Knowledge Reuse for Structured Agent Skills to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.20999; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 755 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L755 | 2026-07-24 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.20999 | [2607.20999] Workflow-Localized Mechanism Learning: Attribution-Guided Repair and Knowledge Reuse for Structured Agent Skills | Agent Skills package reusable procedural knowledge as external artifacts for frozen language-model agents, yet existing optimizers do not jointly resolve where a failure occurs in a workflow, which mechanism caused it, and how relevant knowledge from third-party Skills should be reused locally. We introduce Workflow-Lo... | Zibin Lin; Shengli Zhang; Taotao Wang; Yihan Xia; Deen Ma; Guofu Liao | 2026-07-23 | 2026 | arXiv | arXiv | 8 pages, 3 figures | cs.AI | arxiv-api | 2607.20999 | 2026-08-31T13:57:28 | |||||||
ale-0175 | Research Foundations | research-foundations | Paper | 📄 | Sample-Efficient Learning from Agent Experience | https://arxiv.org/abs/2607.21051 | external | arxiv.org | Uses experience distillation to internalize an agent's own interaction histories into model weights, so in-context experience gains persist after the experience leaves the context window, matching RL-style improvement with far fewer environment interactions. | Uses experience distillation to internalize an agent's own interaction histories into model weights, so in-context experience gains persist after the experience leaves the context window, matching RL-style improvement with far fewer environment interactions. | Uses experience distillation to internalize an agent's own interaction histories into model weights, so in-context experience gains persist after the experience leaves the context window, matching RL-style improvement with far fewer environment interactions. | Context is managed as durable loop state rather than a single prompt payload. Uses experience distillation to internalize an agent's own interaction histories into model weights, so in-context experience gains persist after the experience leaves the context window, matching RL-style improvement with far fewer environme... | Use Sample-Efficient Learning from Agent Experience to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.21051; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 756 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L756 | 2026-07-24 | Learn | learn | Understand the field and its boundaries. | context;state | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.21051 | [2607.21051] Sample-Efficient Learning from Agent Experience | Real-world agent learning is often constrained by costly environment interactions, such as running time-consuming experiments or obtaining human feedback. In-context learning offers a highly sample-efficient way for agents to learn from their own interaction histories, but its gains disappear once that experience is re... | Chenhui Gou; Haoqin Tu; Yunhao Fang; Jianfei Cai; Hamid Rezatofighi | 2026-07-23 | 2026 | arXiv | arXiv | cs.CL | arxiv-api | 2607.21051 | 2026-08-31T13:57:28 | ||||||||
ale-0176 | Research Foundations | research-foundations | Paper | 📄 | PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning | https://arxiv.org/abs/2607.21419 | external | arxiv.org | Policy-centric training paradigm that reframes skills as a dynamic training-time scaffold for long-horizon agent RL: rollout groups from the latest policy become evidence cards, task-specific evaluation adjusts the context for subsequent rollouts, and guidance is pruned as the policy strengthens before the scaffold is ... | Policy-centric training paradigm that reframes skills as a dynamic training-time scaffold for long-horizon agent RL: rollout groups from the latest policy become evidence cards, task-specific evaluation adjusts the context for subsequent rollouts, and guidance is pruned as the policy strengthens before the scaffold is ... | Policy-centric training paradigm that reframes skills as a dynamic training-time scaffold for long-horizon agent RL: rollout groups from the latest policy become evidence cards, task-specific evaluation adjusts the context for subsequent rollouts, and guidance is pruned as the policy strengthens before the scaffold is ... | Evaluation data is used as the feedback signal for improving loop behavior. Policy-centric training paradigm that reframes skills as a dynamic training-time scaffold for long-horizon agent RL: rollout groups from the latest policy become evidence cards, task-specific evaluation adjusts the context for subsequent rollou... | Use PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.21419; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 757 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L757 | 2026-07-24 | Learn | learn | Understand the field and its boundaries. | context;verification;budget | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.21419 | [2607.21419] PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning | In long-horizon LLM agent reinforcement learning, weak policies often repeat similar failures, producing uninformative rollout trajectories and limiting effective policy optimization. Existing skill-centric methods improve exploration by optimizing, filtering, or internalizing reusable skills. However, they remain cent... | Yipeng Shi; Zhipeng Ma; Yue Wang; Qitai Tan; Yang Li; Peng Chen; Zhengzhou Zhu | 2026-07-23 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.21419 | 2026-08-31T13:57:28 | ||||||||
ale-0177 | Research Foundations | research-foundations | Paper | 📄 | From Agent Failures to Text Policies: What Works and What Breaks | https://arxiv.org/abs/2607.20668 | external | arxiv.org | Applies TextGrad-style natural-language policy learning to agent failure trajectories and separates two abilities usually conflated: following a useful text policy versus learning one from experience. | Applies TextGrad-style natural-language policy learning to agent failure trajectories and separates two abilities usually conflated: following a useful text policy versus learning one from experience. | Applies TextGrad-style natural-language policy learning to agent failure trajectories and separates two abilities usually conflated: following a useful text policy versus learning one from experience. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Applies TextGrad-style natural-language policy learning to agent failure trajectories and separates two abilities usually conflated: following a useful text policy versus learning one from experience. | Use From Agent Failures to Text Policies: What Works and What Breaks to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.20668; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 758 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L758 | 2026-07-25 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.20668 | [2607.20668] From Agent Failures to Text Policies: What Works and What Breaks | TextGrad improves language-model systems by revising text from feedback. Its core thesis is that natural-language feedback can act as a gradient for optimizing text components without changing model weights. Applying it to agents is harder because feedback arrives only after a sequence of actions, making it difficult t... | Jaideep Ray; Ankit Goyal | 2026-07-22 | 2026 | arXiv | arXiv | cs.CL | arxiv-api | 2607.20668 | 2026-08-31T13:57:28 | ||||||||
ale-0178 | Research Foundations | research-foundations | Paper | 📄 | The Regression Tax: Decomposing Why Skills Help and Hurt LLM Agents | https://arxiv.org/abs/2607.22520 | external | arxiv.org | Nearly 6,000 runs across two office-automation benchmarks and three model harness stacks measuring what average success rate hides: skills also cause regressions (tasks solved without the skill, failed with it). Finding that should change how people ship Skills -- the best-performing skills win primarily by regressing ... | Nearly 6,000 runs across two office-automation benchmarks and three model harness stacks measuring what average success rate hides: skills also cause regressions (tasks solved without the skill, failed with it). Finding that should change how people ship Skills -- the best-performing skills win primarily by regressing ... | Nearly 6,000 runs across two office-automation benchmarks and three model harness stacks measuring what average success rate hides: skills also cause regressions (tasks solved without the skill, failed with it). Finding that should change how people ship Skills -- the best-performing skills win primarily by regressing ... | The work turns loop quality into a measurable task or score. Nearly 6,000 runs across two office-automation benchmarks and three model harness stacks measuring what average success rate hides: skills also cause regressions (tasks solved without the skill, failed with it). Finding that should change how people ship Skil... | Use The Regression Tax: Decomposing Why Skills Help and Hurt LLM Agents to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.22520; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 759 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L759 | 2026-07-28 | Learn | learn | Understand the field and its boundaries. | verification | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.22520 | [2607.22520] The Regression Tax: Decomposing Why Skills Help and Hurt LLM Agents | Adding procedural skills to an LLM agent is typically evaluated by average improvement in task success. However, this metric hides an important cost: skills can also make agents worse. We measure both sides by comparing agents with and without skills across nearly 6,000 runs spanning two office automation benchmarks an... | Darshan Tank; Baran Nama | 2026-07-24 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.22520 | 2026-08-31T13:57:28 | ||||||||
ale-0179 | Research Foundations | research-foundations | Paper | 📄 | Learning on the Job: Continual Learning from Deployment Feedback for Frozen-Weights Agents | https://arxiv.org/abs/2607.22157 | external | arxiv.org | Shows ordinary production feedback -- one-bit outcome verdicts and after-the-fact corrections -- is a sufficient signal for continual learning when a frozen model is paired with external memory distilling each episode into retrievable natural-language rules. | Shows ordinary production feedback -- one-bit outcome verdicts and after-the-fact corrections -- is a sufficient signal for continual learning when a frozen model is paired with external memory distilling each episode into retrievable natural-language rules. | Shows ordinary production feedback -- one-bit outcome verdicts and after-the-fact corrections -- is a sufficient signal for continual learning when a frozen model is paired with external memory distilling each episode into retrievable natural-language rules. | Persistent memory is treated as an external runtime artifact. Shows ordinary production feedback -- one-bit outcome verdicts and after-the-fact corrections -- is a sufficient signal for continual learning when a frozen model is paired with external memory distilling each episode into retrievable natural-language rules. | Use Learning on the Job: Continual Learning from Deployment Feedback for Frozen-Weights Agents to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.22157; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 760 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L760 | 2026-07-28 | Learn | learn | Understand the field and its boundaries. | context | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.22157 | [2607.22157] Learning on the Job: Continual Learning from Deployment Feedback for Frozen-Weights Agents | AI agents encounter learning opportunities in every episode they run, and discard nearly all of them: the underlying models are frozen at deployment, so an agent that resolves a difficult request today starts from zero when it recurs tomorrow. Yet ordinary operation already produces feedback, in the form of outcome ver... | Valentin Tablan; Scott Taylor; Kristoffer Bernhem | 2026-07-24 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.22157 | 2026-08-31T13:57:28 | ||||||||
ale-0180 | Research Foundations | research-foundations | Paper | 📄 | Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills | https://arxiv.org/abs/2607.22529 | external | arxiv.org | Names the central dilemma of self-evolving training loops: environment-bound methods get precise feedback but stay narrow, while open-ended self-generation broadens the task space and loses reliable verification, letting misleading rewards pollute the training loop. | Names the central dilemma of self-evolving training loops: environment-bound methods get precise feedback but stay narrow, while open-ended self-generation broadens the task space and loses reliable verification, letting misleading rewards pollute the training loop. | Names the central dilemma of self-evolving training loops: environment-bound methods get precise feedback but stay narrow, while open-ended self-generation broadens the task space and loses reliable verification, letting misleading rewards pollute the training loop. | Verification is promoted from a final check to a loop-control signal. Names the central dilemma of self-evolving training loops: environment-bound methods get precise feedback but stay narrow, while open-ended self-generation broadens the task space and loses reliable verification, letting misleading rewards pollute th... | Use Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.22529; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 761 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L761 | 2026-07-28 | Learn | learn | Understand the field and its boundaries. | verification | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.22529 | [2607.22529] Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills | LLM training is shifting from manual design and annotation to interaction-driven self-evolution. However, existing self-evolutionary methods face a fundamental dilemma between task diversity and verification reliability: environment-bound methods obtain precise feedback but confine learning to narrow domains, while ope... | Siyuan Huang; Pengyu Cheng; Haotian Liu; Tao Chen; Yihao Liu; Jingwei Ni; Shijie Zhou; Ziyi Yang; Gangwei Jiang; Mengyu Zhou; Yu Cheng; Xiaoxi Jiang; Guanjun Jiang | 2026-07-24 | 2026 | arXiv | arXiv | cs.CL | arxiv-api | 2607.22529 | 2026-08-31T13:57:28 | ||||||||
ale-0181 | Research Foundations | research-foundations | Paper | 📄 | Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning | https://arxiv.org/abs/2607.21971 | external | arxiv.org | Hypothesizes that AlphaEvolve-style test-time self-evolution succeeds because of meta-skills -- notably self-reflection against environment feedback -- that conventional post-training neglects entirely. | Hypothesizes that AlphaEvolve-style test-time self-evolution succeeds because of meta-skills -- notably self-reflection against environment feedback -- that conventional post-training neglects entirely. | Hypothesizes that AlphaEvolve-style test-time self-evolution succeeds because of meta-skills -- notably self-reflection against environment feedback -- that conventional post-training neglects entirely. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Hypothesizes that AlphaEvolve-style test-time self-evolution succeeds because of meta-skills -- notably self-reflection against environment feedback -- that conventional post-training neglects entirely. | Use Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.21971; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 762 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L762 | 2026-07-28 | Learn | learn | Understand the field and its boundaries. | verification | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.21971 | [2607.21971] Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning | Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains. We hypothesize that the success of such evolution frameworks hinges on meta-skills, such as self-reflection with environment feedback, that enable effective multi-round refin... | Shujin Wu; Cheng Qian; Xiusi Chen; Heng Ji | 2026-07-24 | 2026 | arXiv | arXiv | cs.LG | arxiv-api | 2607.21971 | 2026-08-31T13:57:28 | ||||||||
ale-0182 | Research Foundations | research-foundations | Paper | 📄 | From Execution to Capability: Scientific Experience Consolidation via Procedural Knowledge Synthesis | https://arxiv.org/abs/2607.24459 | external | arxiv.org | Studies why executable feedback on one task rarely becomes durable capability on the next, and names the two obstacles precisely: trajectory-derived artifacts often encode source-specific repairs rather than cross-task mechanisms, and a weaker target model may not be able to operationalize a valid abstract procedure (t... | Studies why executable feedback on one task rarely becomes durable capability on the next, and names the two obstacles precisely: trajectory-derived artifacts often encode source-specific repairs rather than cross-task mechanisms, and a weaker target model may not be able to operationalize a valid abstract procedure (t... | Studies why executable feedback on one task rarely becomes durable capability on the next, and names the two obstacles precisely: trajectory-derived artifacts often encode source-specific repairs rather than cross-task mechanisms, and a weaker target model may not be able to operationalize a valid abstract procedure (t... | Durable execution and replay are treated as first-class loop infrastructure. Studies why executable feedback on one task rarely becomes durable capability on the next, and names the two obstacles precisely: trajectory-derived artifacts often encode source-specific repairs rather than cross-task mechanisms, and a weaker... | Use From Execution to Capability: Scientific Experience Consolidation via Procedural Knowledge Synthesis to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.24459; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 763 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L763 | 2026-07-28 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.24459 | [2607.24459] From Execution to Capability: Scientific Experience Consolidation via Procedural Knowledge Synthesis | Large language models increasingly solve scientific-computing tasks, but executable feedback from one problem rarely becomes durable capability on subsequent problems. We study scientific-computing experience consolidation: converting verified runtime experience into transferable procedural knowledge and persistent mod... | Liwei Dong; Jiahao Zhao; Nan Xu | 2026-07-27 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.24459 | 2026-08-31T13:57:28 | ||||||||
ale-0183 | Research Foundations | research-foundations | Paper | 📄 | Agent-UCT: Upper Confidence Bounds Applied to Trees for Agentic Workflow Optimization with Cost-Awareness | https://arxiv.org/abs/2607.24162 | external | arxiv.org | Scaffold optimization as tree search: extends UCT with a reuse-aware regularization term derived from a bipartite prefix reuse graph, biasing selection toward branches that reuse already-materialized configuration prefixes so the search stops re-executing shared pipeline stages under tight evaluation budgets. | Scaffold optimization as tree search: extends UCT with a reuse-aware regularization term derived from a bipartite prefix reuse graph, biasing selection toward branches that reuse already-materialized configuration prefixes so the search stops re-executing shared pipeline stages under tight evaluation budgets. | Scaffold optimization as tree search: extends UCT with a reuse-aware regularization term derived from a bipartite prefix reuse graph, biasing selection toward branches that reuse already-materialized configuration prefixes so the search stops re-executing shared pipeline stages under tight evaluation budgets. | Control flow is represented as an inspectable graph rather than an opaque prompt loop. Scaffold optimization as tree search: extends UCT with a reuse-aware regularization term derived from a bipartite prefix reuse graph, biasing selection toward branches that reuse already-materialized configuration prefixes so the sea... | Use Agent-UCT: Upper Confidence Bounds Applied to Trees for Agentic Workflow Optimization with Cost-Awareness to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.24162; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 764 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L764 | 2026-07-28 | Learn | learn | Understand the field and its boundaries. | verification;budget;exit | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.24162 | [2607.24162] Agent-UCT: Upper Confidence Bounds Applied to Trees for Agentic Workflow Optimization with Cost-Awareness | Optimizing agentic workflows, such as retrieval-augmented generation (RAG) pipelines, requires navigating a combinatorial space of discrete component choices under tight evaluation budgets. Existing approaches - heuristic search, black-box optimization, and standard tree search methods - do not explicitly exploit the c... | Yang Li; Hai Liu; Dian Shao; Yu Wang; Xiyu Chen; Sergey Volkov; Bozhi Wang; Ziyu Sun; Sihang Liu; Ye Luo; Xiaowei Zhang | 2026-07-27 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.24162 | 2026-08-31T13:57:28 | ||||||||
ale-0184 | Research Foundations | research-foundations | Paper | 📄 | The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation | https://arxiv.org/abs/2607.24720 | external | arxiv.org | Builds a unified controlled multi-turn environment to study where long-horizon planning ability actually comes from, which opaque internet-scale training makes impossible to isolate. | Builds a unified controlled multi-turn environment to study where long-horizon planning ability actually comes from, which opaque internet-scale training makes impossible to isolate. | Builds a unified controlled multi-turn environment to study where long-horizon planning ability actually comes from, which opaque internet-scale training makes impossible to isolate. | The work targets tasks that exceed a single context window or prompt session. Builds a unified controlled multi-turn environment to study where long-horizon planning ability actually comes from, which opaque internet-scale training makes impossible to isolate. | Use The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.24720; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 765 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L765 | 2026-07-28 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.24720 | [2607.24720] The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation | Multi-turn long-horizon planning is critical for foundation model agents, yet how to fundamentally improve it remains unclear. Existing models are trained on uncontrollable and opaque Internet data, making it difficult to identify how planning ability is acquired, shaped, and integrated. To address this challenge, we i... | Tianyi Men; Zhuoran Jin; Kang Liu; Jun Zhao | 2026-07-27 | 2026 | arXiv | arXiv | cs.CL | arxiv-api | 2607.24720 | 2026-08-31T13:57:28 | ||||||||
ale-0185 | Research Foundations | research-foundations | Paper | 📄 | Context Assembly as the Controlled Variable: A Control-Theoretic View of Harness Policies for Frozen LLM Agents | https://arxiv.org/abs/2607.25408 | external | arxiv.org | Formalizes the loop as a frozen inner model wrapped by an outer context policy, making prompt templates, demonstrations, and retrieved context the controlled variable, with stability guarantees and uncertainty calibration for online updates. A rare attempt to give harness engineering actual control-theoretic foundation... | Formalizes the loop as a frozen inner model wrapped by an outer context policy, making prompt templates, demonstrations, and retrieved context the controlled variable, with stability guarantees and uncertainty calibration for online updates. A rare attempt to give harness engineering actual control-theoretic foundation... | Formalizes the loop as a frozen inner model wrapped by an outer context policy, making prompt templates, demonstrations, and retrieved context the controlled variable, with stability guarantees and uncertainty calibration for online updates. A rare attempt to give harness engineering actual control-theoretic foundation... | Context is managed as durable loop state rather than a single prompt payload. Formalizes the loop as a frozen inner model wrapped by an outer context policy, making prompt templates, demonstrations, and retrieved context the controlled variable, with stability guarantees and uncertainty calibration for online updates. ... | Use Context Assembly as the Controlled Variable: A Control-Theoretic View of Harness Policies for Frozen LLM Agents to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.25408; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 766 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L766 | 2026-07-30 | Learn | learn | Understand the field and its boundaries. | context | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.25408 | [2607.25408] Context Assembly as the Controlled Variable: A Control-Theoretic View of Harness Policies for Frozen LLM Agents | A growing body of 2026 work applies control theory to LLM agents: Lyapunov-certified stability for tool-mediated controllers (Prinos et al., "Stable Agentic Control", 2026), sample-complexity bounds for sparse policies over massive discrete tool universes (Majumdar, "Sparse Agentic Control", 2026), and regulatory-contr... | Debjyoti Paul | 2026-07-28 | 2026 | arXiv | arXiv | 6 pages, 2 figures, 1 table. Code and companion paper's data: https://github.com/dpaul0501/context-optimization-rl | cs.AI | arxiv-api | 2607.25408 | 2026-08-31T13:57:28 | |||||||
ale-0186 | Research Foundations | research-foundations | Paper | 📄 | Towards an Agent Operating System - Lessons from Classical and Cloud OS | https://arxiv.org/abs/2607.25076 | external | arxiv.org | Argues agentic systems are pre-standardization and proposes deriving abstractions by extending classical OS and cloud primitives to stochastic, natural-language-mediated execution, the way POSIX and Kubernetes consolidated their eras. A useful frame for where loop infrastructure is heading. | Argues agentic systems are pre-standardization and proposes deriving abstractions by extending classical OS and cloud primitives to stochastic, natural-language-mediated execution, the way POSIX and Kubernetes consolidated their eras. A useful frame for where loop infrastructure is heading. | Argues agentic systems are pre-standardization and proposes deriving abstractions by extending classical OS and cloud primitives to stochastic, natural-language-mediated execution, the way POSIX and Kubernetes consolidated their eras. A useful frame for where loop infrastructure is heading. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Argues agentic systems are pre-standardization and proposes deriving abstractions by extending classical OS and cloud primitives to stochastic, natural-language-mediated execution, the way POSIX and Kubernetes consolidated their eras. A useful fr... | Use Towards an Agent Operating System - Lessons from Classical and Cloud OS to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.25076; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 767 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L767 | 2026-07-30 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.25076 | [2607.25076] Towards an Agent Operating System - Lessons from Classical and Cloud OS | Every major wave of platform software follows the same arc: an initial period of experimentation with competing frameworks and ad-hoc implementations, followed by the articulation of a small set of stable abstractions with well-defined semantics, and finally consolidation around those abstractions into a platform that ... | Gosia Steinder; Hubertus Franke | 2026-07-27 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.25076 | 2026-08-31T13:57:28 | ||||||||
ale-0187 | Research Foundations | research-foundations | Blog | 📝 | Discovering Cryptographic Weaknesses with Claude | https://www.anthropic.com/research/discovering-cryptographic-weaknesses | external | www.anthropic.com | Anthropic research report (2026-07-28) that doubles as one of the most detailed first-party accounts of a multi-day autonomous agent loop. | Anthropic research report (2026-07-28) that doubles as one of the most detailed first-party accounts of a multi-day autonomous agent loop. | Anthropic research report (2026-07-28) that doubles as one of the most detailed first-party accounts of a multi-day autonomous agent loop. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Anthropic research report (2026-07-28) that doubles as one of the most detailed first-party accounts of a multi-day autonomous agent loop. | Use Discovering Cryptographic Weaknesses with Claude to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Contextual source from www.anthropic.com; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 768 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L768 | 2026-07-30 | Learn | learn | Understand the field and its boundaries. | whole-loop | builder | cross-layer | enabling | practitioner-analysis | B | ok | https://www.anthropic.com/research/discovering-cryptographic-weaknesses | Discovering cryptographic weaknesses with Claude \ Anthropic | Anthropic researchers find weaknesses in cryptographic algorithms with Claude Mythos Preview | Anthropic | domain-fallback | 2026-08-31T13:57:28 | ||||||||||||||
ale-0188 | Research Foundations | research-foundations | Paper | 📄 | Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering | https://arxiv.org/abs/2607.28568 | external | arxiv.org | Builds OpenMLE around four composable operators, Draft, Improve, Debug, Crossover, that compose into long-horizon search strategies, and trains a 35B execution-grounded model on them. | Builds OpenMLE around four composable operators, Draft, Improve, Debug, Crossover, that compose into long-horizon search strategies, and trains a 35B execution-grounded model on them. | Builds OpenMLE around four composable operators, Draft, Improve, Debug, Crossover, that compose into long-horizon search strategies, and trains a 35B execution-grounded model on them. | The work targets tasks that exceed a single context window or prompt session. Builds OpenMLE around four composable operators, Draft, Improve, Debug, Crossover, that compose into long-horizon search strategies, and trains a 35B execution-grounded model on them. | Use Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.28568; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 769 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L769 | 2026-08-05 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.28568 | [2607.28568] Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering | Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a concrete, executable testbed for studying this capability. We introduce OpenMLE, an open full-stack system for RSI research in MLE, spanning verifiable task environment... | Junlin Yang; Che Jiang; Yu Fu; Tianwei Luo; Can Ren; Weizhi Wang; Kaikai Zhao; Hongyi Liu; Yuxin Zuo; Yuru Wang; Yuchen Fan; Kai Tian; Zhenzhao Yuan; Xiaojian Lin; Li Sheng; Rushi Qiang; Guoli Jia; Xingtai Lv; Ermo Hua; Dianqiao Lei; Youbang Sun; Ning Ding; Bowen Zhou; Kaiyan Zhang | 2026-07-30 | 2026 | arXiv | arXiv | cs.CL | arxiv-api | 2607.28568 | 2026-08-31T13:57:28 | ||||||||
ale-0189 | Research Foundations | research-foundations | Paper | 📄 | Rehearse: Stepping Back from the Confidence Cliff in Self-Improving Autoresearch | https://arxiv.org/abs/2607.27687 | external | arxiv.org | Names and measures the 'confidence cliff': as a self-improving autoresearch system accumulates successful modifications, its ability to predict which next idea will work collapses from ~83% to ~57%, so the loop degrades exactly when it looks healthiest. | Names and measures the 'confidence cliff': as a self-improving autoresearch system accumulates successful modifications, its ability to predict which next idea will work collapses from ~83% to ~57%, so the loop degrades exactly when it looks healthiest. | Names and measures the 'confidence cliff': as a self-improving autoresearch system accumulates successful modifications, its ability to predict which next idea will work collapses from ~83% to ~57%, so the loop degrades exactly when it looks healthiest. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Names and measures the 'confidence cliff': as a self-improving autoresearch system accumulates successful modifications, its ability to predict which next idea will work collapses from ~83% to ~57%, so the loop degrades exactly when it looks heal... | Use Rehearse: Stepping Back from the Confidence Cliff in Self-Improving Autoresearch to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.27687; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 770 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L770 | 2026-08-05 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.27687 | [2607.27687] Rehearse: Stepping Back from the Confidence Cliff in Self-Improving Autoresearch | Autoresearch improves machine-learning code by proposing changes, running full training jobs, and keeping changes that improve the metric. The efficiency of this loop depends not only on generating ideas, but also on the agent's ability to decide, before spending a training run, whether a proposed modification is likel... | Jiazhen Ji; Shouhong Ding | 2026-07-30 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.27687 | 2026-08-31T13:57:28 | ||||||||
ale-0190 | Research Foundations | research-foundations | Paper | 📄 | Harness-G: A Graph-Structured Harness for Search Agents | https://arxiv.org/abs/2607.27652 | external | arxiv.org | Diagnoses 'retrieval-equivalence collapse' in RL-trained search agents: during Search-R1 training, rollouts for the same question keep emitting distinct query strings while their accumulated evidence sets increasingly overlap, so trajectories converge to utility equivalence and within-group returns carry almost no retr... | Diagnoses 'retrieval-equivalence collapse' in RL-trained search agents: during Search-R1 training, rollouts for the same question keep emitting distinct query strings while their accumulated evidence sets increasingly overlap, so trajectories converge to utility equivalence and within-group returns carry almost no retr... | Diagnoses 'retrieval-equivalence collapse' in RL-trained search agents: during Search-R1 training, rollouts for the same question keep emitting distinct query strings while their accumulated evidence sets increasingly overlap, so trajectories converge to utility equivalence and within-group returns carry almost no retr... | Control flow is represented as an inspectable graph rather than an opaque prompt loop. Diagnoses 'retrieval-equivalence collapse' in RL-trained search agents: during Search-R1 training, rollouts for the same question keep emitting distinct query strings while their accumulated evidence sets increasingly overlap, so tra... | Use Harness-G: A Graph-Structured Harness for Search Agents to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2607.27652; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 771 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L771 | 2026-08-07 | Learn | learn | Understand the field and its boundaries. | context | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.27652 | [2607.27652] Harness-G: A Graph-Structured Harness for Search Agents | Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions using final-answer rewards. Current studies mainly improve training with denser or more structured credit signals, but rarely examine whether retrieval is properly formu... | Yanning Hou; Haoyuan Chen; Sihang Zhou; Xiaoshu Chen; Xirui Liu; Duanyang Yuan; Lingyuan Meng; Siwei Wang; Quan Liu; Jian Huang | 2026-07-30 | 2026 | arXiv | arXiv | Code:https://github.com/7HHHHH/Harness-G | cs.CL | arxiv-api | 2607.27652 | 2026-08-31T13:57:28 | |||||||
ale-0191 | Research Foundations | research-foundations | Paper | 📄 | Self-Harness: Harnesses That Improve Themselves | https://arxiv.org/abs/2606.09498 | external | arxiv.org | Turns harness design into a loop the agent runs on itself: mine model-specific failure patterns from execution traces, propose minimal harness edits tied to those failures, then accept an edit only when it survives regression testing. Argues good harness design is model-specific and so scales poorly by hand. | Turns harness design into a loop the agent runs on itself: mine model-specific failure patterns from execution traces, propose minimal harness edits tied to those failures, then accept an edit only when it survives regression testing. Argues good harness design is model-specific and so scales poorly by hand. | Turns harness design into a loop the agent runs on itself: mine model-specific failure patterns from execution traces, propose minimal harness edits tied to those failures, then accept an edit only when it survives regression testing. Argues good harness design is model-specific and so scales poorly by hand. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Turns harness design into a loop the agent runs on itself: mine model-specific failure patterns from execution traces, propose minimal harness edits tied to those failures, then accept an edit only when it survives regression testing. Argues good... | Use Self-Harness: Harnesses That Improve Themselves to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2606.09498; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 772 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L772 | 2026-08-10 | Learn | learn | Understand the field and its boundaries. | verification | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2606.09498 | [2606.09498] Self-Harness: Harnesses That Improve Themselves | The performance of LLM-based agents is jointly shaped by their base models and the harnesses that mediate their interaction with the environment. Because different models exhibit distinct behaviors, effective harness design is inherently model-specific. Yet agent harnesses are still largely engineered by human experts,... | Hangfan Zhang; Shao Zhang; Kangcong Li; Chen Zhang; Yang Chen; Yiqun Zhang; Lei Bai; Shuyue Hu | 2026-06-08 | 2026 | arXiv | arXiv | https://github.com/qzzqzzb/Self-Harness | cs.CL | arxiv-api | 2606.09498 | 2026-08-31T13:57:28 | |||||||
ale-0192 | Research Foundations | research-foundations | Paper | 📄 | DemoEvolve: Overcoming Sparse Feedback in Agentic Harness Evolution With Demonstrations | https://arxiv.org/abs/2605.24539 | external | arxiv.org | Treats harness evolution as sample-efficient adaptation: change the executable structure around a frozen model instead of its weights. Argues reward-only search breaks down in long-horizon stochastic settings where failures cannot be attributed to a mechanism, and bootstraps from human trajectories. | Treats harness evolution as sample-efficient adaptation: change the executable structure around a frozen model instead of its weights. Argues reward-only search breaks down in long-horizon stochastic settings where failures cannot be attributed to a mechanism, and bootstraps from human trajectories. | Treats harness evolution as sample-efficient adaptation: change the executable structure around a frozen model instead of its weights. Argues reward-only search breaks down in long-horizon stochastic settings where failures cannot be attributed to a mechanism, and bootstraps from human trajectories. | The work targets tasks that exceed a single context window or prompt session. Treats harness evolution as sample-efficient adaptation: change the executable structure around a frozen model instead of its weights. Argues reward-only search breaks down in long-horizon stochastic settings where failures cannot be attribut... | Use DemoEvolve: Overcoming Sparse Feedback in Agentic Harness Evolution With Demonstrations to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2605.24539; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 773 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L773 | 2026-08-10 | Learn | learn | Understand the field and its boundaries. | escalation | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2605.24539 | [2605.24539] DemoEvolve: Overcoming Sparse Feedback in Agentic Harness Evolution with Demonstrations | Agent harness evolution improves frozen language-model agents by modifying the executable structures around them. We study this paradigm as a form of sample-efficient fast adaptation: instead of updating model weights, an agent can acquire task-specific competence by changing its external harness, while leaving the bas... | Lirong Che; Yuzhe yang; Peiwen lin; Chuang wang; Xueqian wang; Jian su | 2026-05-23 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2605.24539 | 2026-08-31T13:57:28 | ||||||||
ale-0193 | Research Foundations | research-foundations | Paper | 📄 | From Failed Trajectories to Reliable LLM Agents: Diagnosing and Repairing Harness Flaws | https://arxiv.org/abs/2606.06324 | external | arxiv.org | Argues outcome-driven harness repair produces broad, poorly scoped edits because it never localizes the fault. Compiles execution traces and harness artifacts into an intermediate representation so a repair can be tied to the mechanism, such as tool interface or lifecycle, that caused the failure. | Argues outcome-driven harness repair produces broad, poorly scoped edits because it never localizes the fault. Compiles execution traces and harness artifacts into an intermediate representation so a repair can be tied to the mechanism, such as tool interface or lifecycle, that caused the failure. | Argues outcome-driven harness repair produces broad, poorly scoped edits because it never localizes the fault. Compiles execution traces and harness artifacts into an intermediate representation so a repair can be tied to the mechanism, such as tool interface or lifecycle, that caused the failure. | Connects Loop Engineering to prior agent-loop and feedback-loop research. Argues outcome-driven harness repair produces broad, poorly scoped edits because it never localizes the fault. Compiles execution traces and harness artifacts into an intermediate representation so a repair can be tied to the mechanism, such as t... | Use From Failed Trajectories to Reliable LLM Agents: Diagnosing and Repairing Harness Flaws to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2606.06324; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 774 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L774 | 2026-08-10 | Learn | learn | Understand the field and its boundaries. | workspace | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2606.06324 | [2606.06324] From Failed Trajectories to Reliable LLM Agents: Diagnosing and Repairing Harness Flaws | LLM agents increasingly rely on agent harness: the runtime infrastructure around the base model that defines execution environments, tool interfaces, context, lifecycle orchestration, observability, verification, and governance. Existing self-improving agents and automatic harness evolution methods mainly improve agent... | Mengzhuo Chen; Junjie Wang; Zhe Liu; Yawen Wang; Haiming Zheng; Qing Wang | 2026-06-04 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2606.06324 | 2026-08-31T13:57:28 | ||||||||
ale-0194 | Research Foundations | research-foundations | Paper | 📄 | AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design | https://arxiv.org/abs/2608.13560 | external | arxiv.org | Meta-harness optimizer that guides a code agent to recursively rewrite its own harness from rollout feedback, against the observation that existing harness paradigms stay static instead of accumulating reusable experience. Instantiated on paper-to-poster generation with the accompanying PosterBench. | Meta-harness optimizer that guides a code agent to recursively rewrite its own harness from rollout feedback, against the observation that existing harness paradigms stay static instead of accumulating reusable experience. Instantiated on paper-to-poster generation with the accompanying PosterBench. | Meta-harness optimizer that guides a code agent to recursively rewrite its own harness from rollout feedback, against the observation that existing harness paradigms stay static instead of accumulating reusable experience. Instantiated on paper-to-poster generation with the accompanying PosterBench. | The work targets tasks that exceed a single context window or prompt session. Meta-harness optimizer that guides a code agent to recursively rewrite its own harness from rollout feedback, against the observation that existing harness paradigms stay static instead of accumulating reusable experience. Instantiated on pap... | Use AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2608.13560; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 775 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L775 | 2026-08-17 | Learn | learn | Understand the field and its boundaries. | whole-loop | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2608.13560 | [2608.13560] AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design | Transforming multimodal sources into condensed and structured media outputs can be fundamentally conceptualized as a long-horizon agentic process centered on a model-harness system. While an ideal harness system should align with human design priors and accumulate reusable experience through empirical exploration to dr... | Yaxin Luo; Haobin Jiang; Jialv Zou; Xu Huang; Wenhao Yan; Haodong Li; Zhengrong Yue; Jing Li; Xiaofu Chen; Xiaohan Zhao; Jiacheng Liu; Jiacheng Cui; Zhiqiang Shen; Xiaotong Li | 2026-08-13 | 2026 | arXiv | arXiv | Tech Report. Code at: https://github.com/Yaxin9Luo/AutoDesign | cs.CV | arxiv-api | 2608.13560 | 2026-08-31T13:57:28 | |||||||
ale-0195 | Research Foundations | research-foundations | Paper | 📄 | Engineering Reliable Coding Agents: Evaluating and Operating the System Around the Model | https://arxiv.org/abs/2608.13867 | external | arxiv.org | Monograph on the gap between evaluating coding agents as models and deploying them as systems, synthesizing 164 scholarly works, 100 practitioner records, 29 benchmark records, and 17 system case records. Treats evaluation and operation as a dependency chain where a gain at one layer often fails to reach end-to-end out... | Monograph on the gap between evaluating coding agents as models and deploying them as systems, synthesizing 164 scholarly works, 100 practitioner records, 29 benchmark records, and 17 system case records. Treats evaluation and operation as a dependency chain where a gain at one layer often fails to reach end-to-end out... | Monograph on the gap between evaluating coding agents as models and deploying them as systems, synthesizing 164 scholarly works, 100 practitioner records, 29 benchmark records, and 17 system case records. Treats evaluation and operation as a dependency chain where a gain at one layer often fails to reach end-to-end out... | Evaluation data is used as the feedback signal for improving loop behavior. Monograph on the gap between evaluating coding agents as models and deploying them as systems, synthesizing 164 scholarly works, 100 practitioner records, 29 benchmark records, and 17 system case records. Treats evaluation and operation as a de... | Use Engineering Reliable Coding Agents: Evaluating and Operating the System Around the Model to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2608.13867; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 776 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L776 | 2026-08-20 | Learn | learn | Understand the field and its boundaries. | verification | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2608.13867 | [2608.13867] Engineering Reliable Coding Agents: Evaluating and Operating the System Around the Model | AI coding agents are commonly evaluated as models but deployed as systems. Their reliability depends not only on model capability, but on the harness, execution state, retrieval, memory and state management, permissions, review interfaces, and resource allocation. This monograph examines those boundaries and develops a... | Stephanie Jarmak | 2026-08-14 | 2026 | arXiv | arXiv | Technical review and engineering monograph, 314 pages, 30 figures. Includes an evidence audit, a companion research artifact with 206 reliability records, and runnable protocols for evaluating and operating AI coding agents. August 2026. Source, companion, and reusable protocols: https://github.com/sjarmak/engineering-... | cs.SE | arxiv-api | 2608.13867 | 2026-08-31T13:57:28 | |||||||
ale-0196 | Research Foundations | research-foundations | Paper | 📄 | Stop Comparing LLM Agents Without Disclosing the Harness | https://arxiv.org/abs/2605.23950 | external | arxiv.org | Formalizes the Binding Constraint Thesis: for long-horizon tasks across models of comparable frontier capability, performance variance is governed more by harness configuration than by model choice, so evaluation protocols systematically misattribute harness-level gains to model improvements. Treats the harness as a cl... | Formalizes the Binding Constraint Thesis: for long-horizon tasks across models of comparable frontier capability, performance variance is governed more by harness configuration than by model choice, so evaluation protocols systematically misattribute harness-level gains to model improvements. Treats the harness as a cl... | Formalizes the Binding Constraint Thesis: for long-horizon tasks across models of comparable frontier capability, performance variance is governed more by harness configuration than by model choice, so evaluation protocols systematically misattribute harness-level gains to model improvements. Treats the harness as a cl... | Evaluation data is used as the feedback signal for improving loop behavior. Formalizes the Binding Constraint Thesis: for long-horizon tasks across models of comparable frontier capability, performance variance is governed more by harness configuration than by model choice, so evaluation protocols systematically misatt... | Use Stop Comparing LLM Agents Without Disclosing the Harness to understand the evidence, vocabulary, and lineage behind recurring agent systems. | Research source arXiv:2605.23950; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 777 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L777 | 2026-08-28 | Learn | learn | Understand the field and its boundaries. | verification;exit | researcher;evaluator | cross-layer | enabling | research-preprint | A | ok | https://arxiv.org/abs/2605.23950 | [2605.23950] Stop Comparing LLM Agents Without Disclosing the Harness | This position paper argues that, for long-horizon tasks evaluated across models with comparable frontier capability, the agent execution harness, namely the infrastructure layer that governs context construction, tool interaction, orchestration, and verification around a language model, is often a stronger determinant ... | Yunbei Zhang; Janet Wang; Yingqiang Ge; Weijie Xu; Jihun Hamm; Chandan K. Reddy | 2026-05-07 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2605.23950 | 2026-08-31T13:57:28 | ||||||||
ale-0197 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Universal Transformers | https://openreview.net/forum?id=HyzdRiR9Y7 | external | openreview.net | Introduces recurrent depth for Transformers by repeatedly applying shared self-attention and transition blocks, with optional per-position adaptive halting; establishes the architectural foundation for later looped models. | Introduces recurrent depth for Transformers by repeatedly applying shared self-attention and transition blocks, with optional per-position adaptive halting; establishes the architectural foundation for later looped models. | Introduces recurrent depth for Transformers by repeatedly applying shared self-attention and transition blocks, with optional per-position adaptive halting; establishes the architectural foundation for later looped models. | Reuses learned computation inside one model inference rather than repeating a full agent run. Introduces recurrent depth for Transformers by repeatedly applying shared self-attention and transition blocks, with optional per-position adaptive halting; establishes the architectural foundation for later looped models. | Use Universal Transformers to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 797 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L797 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-paper | A | ok | https://openreview.net/challenge?redirect=%2Fforum%3Fid%3DHyzdRiR9Y7 | Verifying your browser | OpenReview | Mostafa Dehghani; Stephan Gouws; Oriol Vinyals; Jakob Uszkoreit; Łukasz Kaiser | 2019 | 2019 | International Conference on Learning Representations (ICLR) | OpenReview | Published at ICLR 2019; venue and authors verified from the official OpenReview record. | OpenReview | 2026-08-31T13:57:28 | ||||||||||
ale-0198 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Looped Transformers as Programmable Computers | https://proceedings.mlr.press/v202/giannou23a.html | external | proceedings.mlr.press | Constructs a constant-depth looped Transformer that advances an in-state program counter and executes reusable instructions, showing how iterative algorithms and in-context gradient descent can be represented through repeated shared computation. | Constructs a constant-depth looped Transformer that advances an in-state program counter and executes reusable instructions, showing how iterative algorithms and in-context gradient descent can be represented through repeated shared computation. | Constructs a constant-depth looped Transformer that advances an in-state program counter and executes reusable instructions, showing how iterative algorithms and in-context gradient descent can be represented through repeated shared computation. | Reuses learned computation inside one model inference rather than repeating a full agent run. Constructs a constant-depth looped Transformer that advances an in-state program counter and executes reusable instructions, showing how iterative algorithms and in-context gradient descent can be represented through repeated ... | Use Looped Transformers as Programmable Computers to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 798 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L798 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;context;state | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-paper | A | ok | https://proceedings.mlr.press/v202/giannou23a.html | Looped Transformers as Programmable Computers | Looped Transformers as Programmable ComputersAngeliki Giannou, Shashank Rajput, Jy-Yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris P... | Angeliki Giannou; Shashank Rajput; Jy-Yong Sohn; Kangwook Lee; Jason D. Lee; Dimitris Papailiopoulos | 2023-07-03 | 2023 | International Conference on Machine Learning | PMLR | html-meta | 2026-08-31T13:57:28 | ||||||||||
ale-0199 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Looped Transformers are Better at Learning Learning Algorithms | https://openreview.net/forum?id=HHbRxoDTxE | external | openreview.net | Trains input-injected looped Transformers for in-context data fitting and shows that iterative shared computation can match standard Transformers on tested function classes with substantially fewer parameters. | Trains input-injected looped Transformers for in-context data fitting and shows that iterative shared computation can match standard Transformers on tested function classes with substantially fewer parameters. | Trains input-injected looped Transformers for in-context data fitting and shows that iterative shared computation can match standard Transformers on tested function classes with substantially fewer parameters. | Reuses learned computation inside one model inference rather than repeating a full agent run. Trains input-injected looped Transformers for in-context data fitting and shows that iterative shared computation can match standard Transformers on tested function classes with substantially fewer parameters. | Use Looped Transformers are Better at Learning Learning Algorithms to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 799 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L799 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;context;verification | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-paper | A | ok | https://openreview.net/challenge?redirect=%2Fforum%3Fid%3DHHbRxoDTxE | Verifying your browser | OpenReview | Liu Yang; Kangwook Lee; Robert D. Nowak; Dimitris Papailiopoulos | 2024 | 2024 | International Conference on Learning Representations (ICLR) | OpenReview | Published at ICLR 2024; venue and authors verified from the official OpenReview record. | OpenReview | 2026-08-31T13:57:28 | ||||||||||
ale-0200 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding | https://proceedings.mlr.press/v267/xu25x.html | external | proceedings.mlr.press | Derives approximation rates for looped Transformers, identifies a loop-specific expressivity limit, and uses timestep-conditioned scaling to improve function approximation as recurrence increases. | Derives approximation rates for looped Transformers, identifies a loop-specific expressivity limit, and uses timestep-conditioned scaling to improve function approximation as recurrence increases. | Derives approximation rates for looped Transformers, identifies a loop-specific expressivity limit, and uses timestep-conditioned scaling to improve function approximation as recurrence increases. | Reuses learned computation inside one model inference rather than repeating a full agent run. Derives approximation rates for looped Transformers, identifies a loop-specific expressivity limit, and uses timestep-conditioned scaling to improve function approximation as recurrence increases. | Use On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 800 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L800 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-paper | A | ok | https://proceedings.mlr.press/v267/xu25x.html | On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding | On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep EncodingKevin Xu, Issei SatoLooped Transformers provide ad... | Kevin Xu; Issei Sato | 2025-10-06 | 2025 | International Conference on Machine Learning | PMLR | html-meta | 2026-08-31T13:57:28 |
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