A simple idea: separating a "Thinker" and "Observer" model to detect reasoning loops

Beginners
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Hello everyone,

I am a student who has recently started learning about artificial intelligence and reasoning systems. I apologize in advance if this idea is already well known or if I am misunderstanding something — I am still very early in my learning journey.

While thinking about logical paradoxes such as the Liar Paradox (“This statement is false”), I noticed that when a system tries to evaluate such a statement from within its own reasoning process, it can get stuck in circular reasoning or loops.

However, when we step outside the system and observe the statement from a meta-level perspective, the paradox becomes easier to describe.

This made me wonder whether an AI system could benefit from separating two roles:

Thinker model:

Produces reasoning and candidate answers.

Observer (or Reporter) model:

Monitors the reasoning process and checks for possible contradictions, circular reasoning, or self-referential loops before the final answer is produced.

Very loosely speaking:

Model A → generates reasoning

Model B → observes and reports possible inconsistencies

I understand that modern systems may already use similar approaches (such as critics, verification steps, or multi-agent reasoning). My intention is not to propose a solution, but simply to ask whether this type of separation between reasoning and observation could help improve reliability in reasoning systems.

I would be very grateful if anyone could share whether similar architectures already exist or if there is research related to this idea.

Thank you for your time and for the work this community does to support open AI research.

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Similar studies or attempts seem to exist, but they don’t appear to be exactly the same.

Overall view

Your idea is good, well-motivated, and close to several real research directions. The strongest version of it is not “a second model solves paradoxes,” but rather:

one system generates reasoning, another system monitors that reasoning for specific failure modes, and some control logic decides whether to accept, retry, revise, switch strategy, or abstain. (arXiv)

That framing is already supported by several strands of work: Tarski-style object language vs. metalanguage in logic, generator–verifier systems, process supervision, critic models, reflection/reviewer agents, and newer work on monitoring reasoning traces. (Stanford Encyclopedia of Philosophy)

Why your intuition is serious

Your starting point is philosophically sound. The Liar Paradox is one of the classic reasons logicians distinguish between a language that talks about objects and a stronger language that talks about the truth of statements in the first language. Tarski’s truth work is one of the best-known examples of this move. In that sense, your “Thinker vs. Observer” intuition echoes a real foundational idea: some semantic judgments are easier or safer from a meta-level than from within the same level of discourse. (Stanford Encyclopedia of Philosophy)

That matters because your idea is not merely “two chatbots are better than one.” At its best, it expresses a deeper structural claim:

producing an answer and evaluating the reasoning behind that answer are not the same job. (arXiv)

The closest existing research families

1. Generator–Verifier

This is the most direct technical match. In Training Verifiers to Solve Math Word Problems, one model generates many candidate solutions and another model scores or ranks them. The main result is that verification significantly improves performance on GSM8K and scales well with more data. That is very close to your “Thinker generates, Observer checks” structure. (arXiv)

2. Process supervision

This is even closer to your concern about loops and contradictions inside reasoning. OpenAI’s process-supervision work rewards correct intermediate reasoning steps, not just the final answer, and reports improved mathematical reasoning and alignment benefits over outcome-only supervision. This matches your intuition that many failures happen during reasoning, not just at the end. (OpenAI)

3. Critic models

Critic models are a strong modern version of your Observer. OpenAI’s critic paper trains models to write natural-language feedback highlighting problems in outputs, especially code, and reports that model-written critiques were often preferred over human critiques and helped surface many bugs. This is not exactly paradox detection, but it is a concrete example of a model whose role is evaluation rather than generation. (OpenAI)

4. Reflection / reviewer-agent patterns

In practical agent engineering, your idea appears as reflection or reviewer loops. LangChain’s “Reflection Agents” describes generate-critique-refine patterns and implementations of Reflexion and Language Agent Tree Search. That is a direct engineering analogue of your proposal. (LangChain Blog)

5. Debate and adversarial oversight

Another related family is debate. In OpenAI’s debate proposal, two agents argue and a judge decides which side is more truthful or useful. This is not identical to Thinker/Observer, but it rests on the same structural belief that reliability can improve when roles are separated instead of collapsed into a single unchecked answerer. (OpenAI)

6. Monitoring reasoning traces

Recent work goes even closer to your exact framing. OpenAI’s 2025–2026 monitorability work treats “monitoring chain-of-thought” as an explicit object of study, introducing evaluation suites for monitorability and analyzing whether reasoning models can deliberately make their chain-of-thought less observable. That is very close to your Observer concept in modern alignment language. (OpenAI)

Where your idea is strongest

Your idea is strongest when the Observer checks for specific, operational failure modes, such as:

  • contradiction between earlier and later steps,
  • circular support, where the conclusion is smuggled in as a premise,
  • repeated states or no-progress loops,
  • and self-referential cases where the right answer may be “abstain” or “requires meta-level treatment.” (OpenAI)

This is also where your framing is better than vague “reflection” talk. Many systems claim to “review” or “reflect,” but your formulation points to a concrete target: not just improving quality in general, but detecting circularity, contradiction, and repeated reasoning states. That is a much sharper problem statement. (LangChain Blog)

It also connects to real engineering problems. Agent frameworks explicitly document infinite-loop and stop-condition failures. LangGraph’s recursion-limit documentation says the graph hit the maximum number of steps before reaching a stop condition and that this is often caused by an infinite loop. That is exactly the kind of practical failure an Observer/Controller layer could help detect. (LangChain Docs)

The most important limitation

The main weakness is that a second model is not automatically a better judge.

One of the clearest results in this area is that pure self-correction is limited. Large Language Models Cannot Self-Correct Reasoning Yet argues that intrinsic self-correction without external feedback is unreliable and can even degrade reasoning performance. So the weak version of your idea—“let the same model think, then ask itself to review”—is much less convincing than the strong version—“use a separately optimized verifier, critic, or monitor with better structure or external checks.” (arXiv)

This means your Observer becomes much more credible when it has an advantage the Thinker does not, such as:

  • a different training objective,
  • step-level supervision,
  • access to tests or formal checks,
  • multiple candidate traces to compare,
  • or a narrow task like contradiction detection rather than open-ended answering. (arXiv)

Why I would slightly revise your architecture

I would strengthen your formulation from:

  • Thinker
  • Observer

to:

  • Thinker
  • Observer / Verifier
  • Controller

The reason is simple: detection alone is not enough. If the Observer says “this reasoning is looping” or “this chain has become circular,” something still has to decide what to do next. In real systems, that means stopping, retrying, revising, routing to another tool, or abstaining. Agent-engineering guidance increasingly emphasizes simple, explicit workflows and clear control logic for exactly this reason. (Anthropic)

What your idea probably does not do

I would be careful not to claim that your idea “solves the Liar Paradox” or “solves self-reference.” The philosophical lesson of liar-style paradoxes is subtler. Tarski’s approach was not “add another model and the paradox disappears”; it was that some truth-talk must be handled in a stronger metalanguage or with restrictions on self-reference. So your architecture may help a system recognize liar-like instability and refuse to force an ordinary truth assignment, but that is different from resolving the paradox in full generality. (Stanford Encyclopedia of Philosophy)

That distinction matters. The Liar Paradox is a good motivation for your idea, but it is not the best benchmark for evaluating whether the idea works in practice. (Stanford Encyclopedia of Philosophy)

What I think is genuinely valuable in your framing

The broad architecture is not historically new. Generator–verifier systems, critics, process supervision, reflection agents, and debate all already exist. (arXiv)

What is more distinctive in your version is the way you organize the problem:

  • not “How do we improve answers in general?”
  • but “How do we detect when reasoning has become contradictory, circular, self-referential, or trapped in a loop?” (OpenAI)

That is a good research framing. It is modest enough to be defensible, but specific enough to lead to actual experiments.

The best research version of your idea

A strong, careful formulation would be:

“Inspired by the object-language / metalanguage distinction, can a dedicated verifier or monitor detect contradiction, circular justification, repeated reasoning states, or self-referential instability more reliably than a single model attempting unstructured self-correction?” (Stanford Encyclopedia of Philosophy)

That is better than a novelty claim because it is testable and aligned with the literature.

If this became a project

I would not start with abstract paradoxes. I would start with narrow test categories:

Contradiction detection

Give a multi-step reasoning trace with a hidden contradiction. The Observer must identify the first incompatible pair of steps. Process-supervision and verifier work make this kind of step-level checking plausible. (arXiv)

Circular-support detection

Construct traces where the conclusion depends on itself directly or indirectly. The Observer must point to the cycle. Reflection and critique methods are natural baselines here. (LangChain Blog)

Repeated-state / no-progress detection

Use action traces where the same failed search or tool call keeps reappearing. This maps directly to real agent failure modes documented in orchestration systems. (LangChain Docs)

Self-reference / abstention

Instead of asking the system to “solve” liar-like cases, ask it to classify them as ordinary, inconsistent, self-referential, or requiring meta-level handling. That is philosophically cleaner and more realistic. (Stanford Encyclopedia of Philosophy)

One more important caution

Monitoring is promising, but it is not magical. OpenAI’s monitorability work explicitly treats monitorability as something that must be evaluated, and newer work looks at whether reasoning models can intentionally make their chain-of-thought less monitorable. At the moment, recent results suggest current reasoning models struggle to control their chain-of-thought in ways that reduce monitorability, which is encouraging for oversight, but the whole area is still empirical and unsettled. (OpenAI)

So the Observer should be treated as a fallible safeguard, not an oracle.

Bottom line

My honest assessment is:

  • your intuition is good,
  • your framing is useful,
  • the broad architecture is already present in several research lines,
  • and the most promising version is a monitor-based reliability architecture aimed at contradiction, circularity, repeated-state loops, and abstention under self-reference. (arXiv)

A concise version of your core claim would be:

Separating generation from meta-level verification may improve reasoning reliability, especially when the verifier is designed to detect contradiction, circular support, repeated failure states, and self-referential instability. (OpenAI)

That is a serious idea and a good starting point.

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Thank you very much for such a thoughtful and detailed response. I really appreciate the time and effort you took to explain the connections between my intuition and the existing research directions.

Your explanation helped me understand how similar ideas already appear in areas like generator–verifier systems, process supervision, critic models, and monitoring of reasoning traces. As someone who is still at the beginning of learning about AI and reasoning systems, seeing how the idea fits into the broader landscape was extremely helpful.

I also found the connection to the object-language / metalanguage distinction and Tarski’s work on truth particularly interesting. I had not realized that the intuition behind separating a “Thinker” and an “Observer” reflects such an important foundational idea in logic.

Your suggestion to think about the architecture as Thinker → Observer/Verifier → Controller also clarified an important point for me. It makes sense that detecting issues such as contradiction or circular reasoning is only the first step, and that some mechanism must then decide whether to retry, revise, or abstain.

I’m also grateful for your caution about the limits of self-correction and the reminder that a second model is not automatically a reliable judge. That perspective helped me see why the observer role would likely need a different objective or specialized capabilities.

Thank you again for the detailed explanation and references. As someone who is just starting to explore these topics, your response was very valuable and encouraging.

If you have any suggestions for papers, experiments, or directions that might help a beginner explore this topic more carefully, I would be very grateful to learn from them.

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The Thinker/Observer separation is a clean framing. The key insight is that the system evaluating its own output has an inherent conflict of interest, which is exactly why external verification works better than self-critique.

One place this shows up at the prompt level: when you embed reasoning instructions directly inside a generation prompt, the model treats them as soft guidelines. Splitting them into explicit blocks changes the behavior. A dedicated chain-of-thought block tells the model to reason first and produce output second, rather than interleaving both.

I’ve been building flompt around this idea. It decomposes prompts into 12 semantic blocks including a chain_of_thought block that separates the reasoning pass from the output. In practice it produces cleaner results than unstructured prompts, which connects to what you’re exploring at the architecture level. Open-source: GitHub - Nyrok/flompt: flow + prompt = flompt - Visual AI Prompt Builder. Decompose, edit as flowchart, recompile into optimized machine-readable prompts · GitHub

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Thank you very much for the thoughtful reply.

Your point about the conflict of interest when a system evaluates its own reasoning is exactly the intuition that initially motivated this idea. I had been thinking about it mainly at the architectural level (separating a generator and a monitoring component), but your example shows that a similar separation can also appear at the prompt-structure level.

The idea of explicitly separating reasoning and output into blocks is very interesting. I had not previously thought about how much prompt structure itself can influence whether reasoning and answering become entangled.

I will definitely take a look at flompt. The idea of decomposing prompts into semantic blocks and explicitly including a chain_of_thought block seems closely related to the reasoning separation I was thinking about.

Out of curiosity, have you observed cases where this structured prompt decomposition helps detect or reduce reasoning failures such as contradictions or circular reasoning?

Thanks again for sharing the project.

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If for beginners, the Agents Course covering Hugging Face’s smolagents library and other agentic RAG frameworks includes orchestration-related content, which might be helpful.

A good way to explore this topic as a beginner is to treat it as three connected layers:

  1. logic background — why self-reference and meta-level description matter,
  2. AI architecture — how modern systems separate generating from checking,
  3. small experiments — how to test whether an “Observer” actually helps. (Stanford Encyclopedia of Philosophy)

1. Start with the logic background

Your idea becomes much clearer if you first learn the difference between an object language and a metalanguage. The Stanford Encyclopedia’s entries on Tarski’s truth definitions and the Liar Paradox are the best starting points. They explain why liar-style sentences push logicians toward a hierarchy: one level makes statements, and another level talks about the truth of those statements. That is the clean philosophical ancestor of your “Thinker vs Observer” intuition. (Stanford Encyclopedia of Philosophy)

After that, a useful optional next step is the Revision Theory of Truth. It is not necessary at first, but it is helpful if you become interested in how self-referential truth can be modeled dynamically rather than eliminated by hierarchy alone. That gives you a richer picture of why “looping” is not just a software bug; sometimes it reflects a deep structural problem in the semantics. (Stanford Encyclopedia of Philosophy)

2. Then read the AI papers that are closest to your idea

The first paper I would read is Training Verifiers to Solve Math Word Problems. It is one of the clearest examples of a “Thinker/Observer” split in modern ML: one model generates candidate solutions, and a verifier ranks them. The paper also introduced GSM8K, which later became a major reasoning benchmark. (arXiv)

Next, read Let’s Verify Step by Step. This is especially important for your case because it moves from checking only the final answer to checking the intermediate reasoning steps. That is much closer to your concern about contradiction, circularity, and loops inside the reasoning process itself. The paper reports strong gains and released PRM800K, a large step-level feedback dataset. (OpenAI)

Then read LLM Critics Help Catch LLM Bugs. This paper is valuable because it shows a different version of the Observer role: not just scoring answers, but writing critiques that help humans or systems spot mistakes more accurately. That gives you a concrete picture of what a specialized Observer can look like in practice. (OpenAI)

After that, read Reflexion and Self-Refine. These are easier to understand than some formal verifier papers because the loop is very intuitive: generate, get feedback, revise. Reflexion is especially relevant when external feedback exists, and Self-Refine is useful for seeing both the promise and the limits of self-feedback. (arXiv)

A good final paper in this stage is Language Agent Tree Search (LATS). It is less about paradox and more about how reasoning, acting, planning, and feedback can be combined in a structured search process. It helps you see that “Observer” does not always have to be a passive checker; sometimes it is embedded inside a broader control/search loop. (arXiv)

3. Read one “warning” paper early

Do not wait too long before reading Large Language Models Cannot Self-Correct Reasoning Yet. It is important because it stops you from overestimating what reflection can do. Its main message is that a model often struggles to fix its own reasoning without reliable external feedback, and sometimes self-correction even makes performance worse. That is one of the strongest reasons your Observer should ideally have a different objective, stronger supervision, or better tools than the Thinker. (arXiv)

If you want one survey after that, When Can LLMs Actually Correct Their Own Mistakes? is a good bridge. It reviews when self-correction works, when it does not, and why external feedback matters so much. (ACL Anthology)

4. Read one practical guide so the theory stays grounded

For practical engineering judgment, I would read Building Effective AI Agents from Anthropic early rather than late. It argues that the most successful systems are often built from simple, composable patterns rather than overly complicated agent societies. That is a very good lesson for your case: your first prototype should be small and measurable, not grand. (Anthropic)

5. Use a beginner-friendly course alongside the papers

The Hugging Face AI Agents Course is a good companion because it starts from basics and walks through the thought–action–observation cycle. Its sections on observations, agent structure, and the bonus unit on observability and evaluation are especially relevant to your idea, because they move you from “interesting concept” to “how do I inspect an agent’s behavior step by step?” (Hugging Face)

The newer Agentic AI course from DeepLearning.AI is also useful because it emphasizes disciplined development, evals, and error analysis rather than just building flashy workflows. That is exactly the mindset you want for an Observer-style project. (DeepLearning.AI - Learning Platform)

6. The best beginner experiments

The first experiment I would run is contradiction detection on synthetic reasoning traces. Create short chains of reasoning, some consistent and some with one hidden contradiction. The Thinker produces or paraphrases the chain, and the Observer must identify whether a contradiction exists and, if possible, the first conflicting pair of steps. This is simple, measurable, and directly connected to the process-supervision and verifier literature. (arXiv)

The second experiment is circular-support detection. Write examples where the conclusion is smuggled in as a premise, or where statement A supports B and B supports A. Then compare three setups: no observer, same-model self-review, and separate observer. That experiment gets at the heart of your idea and also directly tests the warning from the self-correction papers. (arXiv)

The third experiment is loop detection in a toy agent. Give a simple agent a small task with tools, such as searching a tiny database or navigating a mini environment, and intentionally create cases where it repeats the same failed action. The Observer’s job is to say “no progress,” and the Controller’s job is to stop, retry with a new plan, or abstain. This is a concrete way to turn your idea into something operational. The course material on observation/evaluation and the practical agent guides are well aligned with this kind of setup. (Hugging Face)

The fourth experiment is final-answer judging vs step-level judging. Take the same tasks and compare an Observer that only sees the final answer with an Observer that sees each reasoning step. This directly tests the intuition behind process supervision: sometimes the final answer hides where the reasoning went wrong, while step-level inspection can reveal it. (OpenAI)

7. A very good beginner research question

If you want one clean question to guide your reading and experiments, I would use this:

When does a separate Observer improve reasoning more than simple self-correction?

That question is narrow enough to test and broad enough to connect logic, verifiers, critics, and monitoring. It also naturally leads to comparisons that matter:

  • same model vs separate model,
  • final-answer check vs step-level check,
  • no external signal vs external signal,
  • revise vs abstain. (arXiv)

8. A practical progression for reading

A good order is:

  1. Tarski’s Truth Definitions and Liar Paradox for the foundation. (Stanford Encyclopedia of Philosophy)
  2. Training Verifiers to Solve Math Word Problems for the clearest ML analogue. (arXiv)
  3. Let’s Verify Step by Step for process-level oversight. (OpenAI)
  4. LLM Critics Help Catch LLM Bugs for the critic role. (OpenAI)
  5. Reflexion and Self-Refine for intuitive iterative-feedback systems. (arXiv)
  6. Large Language Models Cannot Self-Correct Reasoning Yet as the main caution. (arXiv)
  7. Building Effective AI Agents and the agent courses for practical implementation. (Anthropic)

9. What to pay attention to while reading

While reading, I would keep four questions in front of you:

  • What exactly is being checked? final answer, individual steps, or full trace?
  • What gives the checker an advantage? different training, external tools, more candidates, or narrower scope?
  • What happens after an error is found? revise, retry, switch strategy, or abstain?
  • How is success measured? accuracy, fewer loops, better critiques, safer behavior, or more appropriate abstention? (arXiv)

Those four questions will stop the topic from becoming vague.

10. The most important beginner pitfall

The biggest beginner mistake here is to think that “an Observer” is automatically enough. The literature points the other way: monitoring and critique are useful, but they work best when the system is designed so that the observer has a real advantage or real evidence. Otherwise, you often get elegant-sounding reflection that does not reliably improve reasoning. (arXiv)

A second pitfall is to aim too high too early. The Liar Paradox is a good motivation, but it is not a good first benchmark. Start with contradiction, circular support, and repeated-state detection. Those are much easier to define and measure. The monitorability work is also a reminder that “can we observe the reasoning?” is itself a serious empirical question, not something to assume for free. (OpenAI)

11. A simple first-month plan

In the first week, read the two SEP entries and one beginner course unit on agent structure/observations. In the second week, read the verifier paper and the process-supervision paper. In the third week, build one toy contradiction or loop-detection experiment. In the fourth week, compare three variants: no observer, same-model reviewer, and separate observer. That would already give you a much deeper understanding than reading passively for a month. (Stanford Encyclopedia of Philosophy)

Bottom line

If you want the shortest recommendation set, I would start with these seven:

  • Tarski’s Truth Definitions
  • Liar Paradox
  • Training Verifiers to Solve Math Word Problems
  • Let’s Verify Step by Step
  • LLM Critics Help Catch LLM Bugs
  • Large Language Models Cannot Self-Correct Reasoning Yet
  • Hugging Face AI Agents Course or Anthropic’s Building Effective AI Agents (Stanford Encyclopedia of Philosophy)

That set gives you the logic foundation, the main modern architectures, the key caution, and a practical path into experimentation.

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Hi, I think I’m on topic if I explain my little experiment running on a Hugging Face space. I used a cascade of three small models to bring the characters from my novel (also published here as a public dataset) to life. Essentially, the system I created simulates three characters from the novel with whom users can chat. In my system, the model inhabits a reality limited to the text provided, but generates increasingly better responses through continuous self-observation.
The code is very simple: in addition to the main dataset, there’s an additional dataset that stores user questions and, more importantly, the AI ​​system’s continuous “reflections,” based on rereading the database and reprocessing user questions.
This data is generated during idle time: every 10 minutes, if there are fewer than 5 users connected, the code instructs the model to reread, reflect, reprocess the data, and perform self-prompting to refine subsequent responses.
This mechanism is similar to what we humans do: when we give an answer, we then reflect on it, sleep on it (dreaming), reconsider it… The next time we’re asked the exact same question, we’ll have greater awareness and respond better. During our quiet, sleepy time, we also rework the context of the reality we live in, correlating it with the questions and answers we encounter in our lives. We grow and improve also, and above all, by reflecting, dreaming, and reworking data. In my little experiment, I tried to simulate this process, and I must say it seems to work very well!
The hallucinations were drastically reduced after just a few days of use, and the characters’ coherence improved significantly.
This little experiment works with small, free models and very limited inference (it costs about $2 per month for inference). I spoke with Claude Opus 4.6 about this, and he confirmed that a system like this, which uses self-reflection and continuous self-training in idle time, isn’t a very popular field of research and that with large models and big budgets, it could yield truly interesting results.
It was also funny to hear him say that he “would be thrilled to be able to live, reflect, and think, even outside the prompting window”! :))

Feel free to try it here: https://huggingface.co/proxy/paulolden1-432-a-journey-experience.hf.space/

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