What Is An AI Doom Loop? Why Reasoning Models Get Stuck

An AI doom loop occurs when a language model repeatedly revisits the same reasoning step, correction or phrase without making meaningful progress towards an answer.

Instead of recognising that it lacks the necessary information or needs a different approach, the model continues thinking in circles. It may repeatedly say things such as “wait,” “alternatively” or “let me reconsider,” consuming more tokens without solving the original problem.

Doom loops are particularly associated with reasoning models that generate extended internal or visible thinking traces. They are more likely to appear during difficult mathematical, coding and factual tasks where the model is uncertain but continues trying to produce a confident answer.

Recent research suggests there may be more than one kind of AI doom loop. Some models fall into obvious textual repetition, while others continually revise their reasoning without repeating exactly the same words.

AI Doom Loops Explained

A normal reasoning process should move through a series of steps. Although modern large language models can produce convincing multi-stage reasoning, their responses are still generated by predicting sequences of tokens rather than understanding a problem exactly as a person would.

A successful reasoning process should:

1. Interpret the question.

2. Identify the relevant information.

3. Test one or more possible solutions.

4. Reject unsuccessful approaches.

5. Produce a final answer.

A model caught in a doom loop becomes trapped around the third or fourth stage.

It might identify a possible answer, doubt it, return to an earlier step, reach the same conclusion and doubt it again. Every trip around the loop reinforces the text and reasoning that came before it.

A simplified example might look like this:

The answer could be 42. Wait, I should verify that calculation. Recalculating gives 42, but perhaps I used the wrong assumption. Let me reconsider. Using the original assumption gives 42. Wait, I should verify that calculation...

The output appears thoughtful because the model is checking its work. However, no new evidence or strategy is being introduced. The model is simply circulating through the same local pattern.

What Does an AI Doom Loop Look Like?

Not every long answer or self-correction is a doom loop. Revising an initial response can be a useful part of AI reasoning.

The warning sign is repetition without progress.

Common signs include:

• Repeating words such as “wait,” “but,” “so” or “alternatively”

• Reconsidering the same conclusion several times

• Returning to an earlier step without introducing new information

• Repeating a paragraph, sentence or code change

• Oscillating between two possible answers

• Continually promising to verify the result

• Using the entire thinking or token budget without answering

• Making and reversing the same edit repeatedly

A coding agent can also enter a practical doom loop. For example, it may edit a file, run a failing test, reverse the edit and then repeat the original change. The text may not be identical each time, but the agent’s actions form a loop.

What Does the Latest AI Doom-Loop Data Show?

Published experiments suggest that targeted training can significantly reduce repetitive doom loops, although results vary by model and testing method.

The LFM and Qwen figures come from Liquid AI’s Antidoom experiments. The Gemma results come from the June 2026 paper Can Editing 1 Neuron Fix Repetition Loops in LLMs?.

Why Do AI Models Get Stuck in Doom Loops?

Large language models generate responses by predicting likely sequences of tokens. They do not always have an independent mechanism that tells them when a line of reasoning is no longer productive.

Several factors can combine to create a doom loop.

1. The Model Is Uncertain

Doom loops commonly begin when a model encounters a problem close to the edge of its knowledge or reasoning ability.

It may know enough to attempt the task but not enough to complete it confidently. Instead of stopping or admitting uncertainty, it repeatedly searches through similar possibilities.

Researchers studying repetition in Gemma 4 models described a non-convergent state in which larger models repeatedly corrected themselves over facts they could not accurately recall. The model continued reasoning, but it could not generate the missing knowledge required to finish the task.

This highlights the difference between producing an impressive reasoning trace and genuinely understanding a problem. Our guide examining whether AI can think for itself explores that distinction in more detail.

2. Previous Text Reinforces the Loop

Once a model begins repeating a sequence, that sequence becomes part of its context.

The existing repetition can make another similar continuation increasingly likely. Each pass strengthens the local pattern until the model has difficulty selecting a genuinely different direction.

Research into circular reasoning in large reasoning models suggests that semantic repetition can begin before obvious textual repetition. In other words, a model may become stuck on the same idea before it starts repeating the same words.

This also helps explain why telling a looping model to “try again” does not always work. The instruction is added to a context already filled with the unsuccessful reasoning pattern.

3. Common Reasoning Phrases Become Fallback Tokens

Terms such as “wait,” “alternatively” and “let’s reconsider” normally serve a useful purpose. They allow a reasoning model to check its work or change direction.

However, these phrases are also highly probable continuations when a model is uncertain.

Liquid AI found that tokens corresponding to words including “So,” “Alternatively,” “Wait” and “But” frequently appeared at the beginning of repetitive loops in one of its experimental reasoning models.

The problem is not the words themselves. The loop begins when the model uses one of them to restart the same reasoning pattern without adding anything new.

4. Low-Temperature Generation Can Reinforce the Loop

Temperature influences how predictable or varied a model’s output is.

At a very low temperature, the model repeatedly selects the most probable next token. This can improve consistency, but it also makes escaping a strongly reinforced pattern more difficult.

Increasing the temperature may introduce an alternative continuation, although this is not a guaranteed solution. Once the probability of the repeating sequence becomes dominant, the model may remain trapped even with more varied sampling.

This limitation is closely related to the broader problem of neural text degeneration. Earlier peer-reviewed research into language-model degeneration demonstrated how common decoding strategies can produce repetitive or unhelpful output even when the generated text initially appears fluent.

5. The Task Requests Too Much Unsupported Recall

Long factual lists are a notable trigger.

A model may successfully recall most items in a large set but struggle with the remaining entries. Rather than admitting that it cannot remember them, it can begin repeating existing items or continually revising its list its.

In a June 2026 study of repetition loops, researchers tested Gemma 4 models on tasks such as listing all 88 recognised constellations or the original 151 Pokémon. Some tested models produced repetitive failures at rates of up to 95%, depending on the task and configuration.

The research found that these failures could survive prompt rewording, changes to the inference engine and many adjustments to sampling settings.

Are All Doom Loops the Same?

The term “doom loop” is still developing, and current research uses it to describe several closely related behaviours.

Type of loop	What happens
Textual repetition loop	The model repeats the same phrase, sentence or reasoning span
Circular self-correction	The wording changes, but the model repeatedly revisits the same unresolved uncertainty
Semantic loop	The model remains focused on the same idea without introducing a new approach
Agent action loop	An AI agent repeats the same edits, searches or tool actions

Liquid AI uses doom loop primarily for repetitive degeneration in which a span repeats until the available context is exhausted.

The Gemma 4 research makes a narrower distinction. It separates ordinary repetitive loops from a deeper doom-looping state in which a model continually self-corrects over a fact it cannot recall.

Both describe the same wider failure: computation continues, but useful progress has stopped.

Can Changing a Single AI Neuron Stop a Doom Loop?

Surprisingly, researchers have demonstrated that certain repetition patterns can be linked to extremely small parts of a model.

The authors of the Gemma 4 study used layer ablation and neuron attribution to locate components associated with repetitive output. In one smaller model, changing the behaviour of a single neuron was enough to suppress the tested repetition pattern while largely preserving performance on general benchmarks.

Larger models required changes involving several neurons or routed experts.

The ability to inspect and modify model behaviour is one reason researchers value open-weight systems. However, users must still weigh that flexibility against the managed safeguards and convenience of proprietary platforms, as explored in our comparison of open-source and closed AI models.

Changing the identified neurons did not completely solve circular self-correction. Editing the part of a model responsible for producing a repetitive pattern cannot give the model a fact it never accurately learned.

Removing a loop may be technically possible, but doing so does not necessarily provide the missing knowledge that caused the original uncertainty.

What Is Antidoom?

Antidoom is a training method developed by Liquid AI to reduce repetitive doom loops in reasoning models.

Instead of broadly penalising every repeated word, Antidoom identifies the precise token at which a loop begins. It then trains the model to prefer several coherent alternatives at that position.

The approach uses a method called Final Token Preference Optimization, or FTPO. Its goal is to alter the small part of the model’s output distribution responsible for initiating the loop without unnecessarily changing the rest of its behaviour.

According to Liquid AI’s published results:

• Doom loops in an early LFM2.5-2.6B checkpoint fell from 10.2% to 1.4%.

• Doom loops in Qwen3.5-4B fell from 22.9% to 1% under greedy sampling.

• Evaluation scores improved because more responses reached an answer instead of becoming trapped.

Developers can inspect the implementation, training pipeline and recommended settings in the open-source Antidoom repository.

How Can You Stop an AI Doom Loop?

Ordinary users cannot edit model weights, but they can interrupt the pattern and restart the task more effectively.

Stop the Current Response

Allowing a visibly repetitive response to continue usually wastes tokens and time. Stop the generation once it is clear that no progress is being made.

Start a New Conversation

The existing repetition forms part of the model’s context. Asking it to try again in the same conversation can cause it to return to the reinforced pattern.

A new conversation provides a clean context without the previous circular reasoning.

Supply the Missing Information

If the model is struggling to remember a precise fact, document or list, give it a reliable source rather than asking it to continue guessing.

Retrieval and web search can help because they introduce information that is not available in the model’s existing reasoning trace.

Require a Different Strategy

A useful recovery prompt is:

Stop the current approach. Identify the exact unresolved assumption, then solve the task using a materially different method. If required information is unavailable, state that instead of continuing.

This explicitly permits the model to admit that it cannot complete the task.

Break the Task Into Smaller Parts

Rather than requesting a large factual list or complex solution in one response, divide it into sections that can be independently checked.

For coding tasks, ask the agent to:

1. Explain the cause of the failure.

2. Propose one change.

3. Apply the change.

4. Run a specific test.

5. Stop if the same test fails twice.

A clear stopping condition can prevent an autonomous agent from repeating an unsuccessful action indefinitely.

Ask for External Verification

For factual questions, instruct the model to verify uncertain claims using an authoritative source. For calculations, request a second method or a small executable test.

Verification is most useful when it adds new evidence. Simply asking the model to “check again” may start another pass around the same loop.

Are Doom Loops the Same as Hallucinations?

No. A hallucination occurs when an AI system presents incorrect or invented information as though it were reliable.

A doom loop occurs when the system repeatedly generates or revisits the same unproductive reasoning pattern.

The two can overlap. A model may become uncertain about a hallucinated fact and repeatedly attempt to verify it using its own previous output. However, an AI can hallucinate without looping, and it can loop without inventing a fact.

Why AI Doom Loops Matter

Doom loops are more than an irritating output problem.

For a chatbot, a loop might waste a few seconds and several thousand tokens. For an autonomous agent, the consequences can be more serious.

An agent with access to files, code, browsers or business systems could repeatedly perform the same unsuccessful action. This can increase API costs, consume computing resources, alter files unnecessarily or prevent a workflow from completing.

Reliable agents therefore need more than capable language models. They also need:

• Loop detection

• Token and time limits

• Action budgets

• Independent success checks

• Clear stopping conditions

• Access to external information

• Human approval for consequential actions

The latest AI systems can spend more time reasoning through difficult problems, use tools and complete longer workflows. Our overview of GPT-5.6 Sol, Terra and Luna explains how current models balance deeper reasoning with speed and cost.

As AI systems are given longer reasoning budgets and greater autonomy, recognising when they are no longer making progress will become an increasingly important part of agent design.

The Bottom Line

An AI doom loop happens when a model continues generating without moving closer to a solution.

Some doom loops are obvious repetitions. Others are more subtle: the model keeps changing its wording while returning to the same unresolved thought. Both demonstrate that longer reasoning does not automatically produce better reasoning.

Research has shown that particular repetition patterns can sometimes be dramatically reduced through highly targeted model edits or preference training. The harder problem is teaching an AI system to recognise when it lacks the information needed to proceed.

The most reliable solution may therefore combine better-trained models with external information, loop detection, independent verification and a simple but essential ability: knowing when to stop.