mlsanityData to 14 Oct 2026

The belief-action gap in language model agents

arXiv 2610.17444 ↗First posted 12 Oct 2026cs.LG cs.AI cs.CLcited by 0

On the map: LLM agents and tool use → Reinforcement learning for LLM agents also fits: General LLM agent frameworks and benchmarks

Abstract (arXiv)

Language model (LM) agents can fail for different reasons, requiring different explanations and interventions. We study how state beliefs, action values, and action preferences interact as LM agents reason and commit to decisions in text-rendered grid worlds. We estimate task-relevant beliefs using report-based and representation-based probes, and action values using inverse reinforcement learning. We find that task-relevant state and value information is often represented more reliably than it is reflected in behaviour. Tracking action preferences throughout reasoning further shows that decisions sharpen around a commitment point and reported belief uncertainty decreases near commitment, although individual belief changes do not consistently explain shifts in action preference. Finally, forced actions follow reasoning prefixes from other decisions when transplanted, whereas the tested reasoning-window activation edits provide limited control over the action. Together, these results suggest that some failures arise not from missing task-relevant information, but from how available information is translated into action. Our measurements distinguish information availability from belief-action consistency without by themselves identifying the cause of each failure. Our code is available at: https://anonymous.4open.science/r/belief-action-gap-F745/

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 30 of its 33 references that are in the map. First posted on arXiv 12 Oct 2026.

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30 of 33 references.

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  1. 2025LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities22 Apr

Other references · 29

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  4. 2016Understanding intermediate layers using linear classifier probescited by 1,353
  5. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  6. 2023Steering Language Models With Activation Engineeringcited by 922
  7. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 876
  8. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 703
  9. 2019Designing and Interpreting Probes with Control Taskscited by 640
  10. 2023Eliciting Latent Predictions from Transformers with the Tuned Lenscited by 623
  11. 2022Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Taskcited by 431
  12. 2023Towards Best Practices of Activation Patching in Language Models: Metrics and Methodscited by 359
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  3. 2024LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinationscited by 244
  4. 2023Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Modelscited by 243
  5. 2025Thought Anchors: Which LLM Reasoning Steps Matter?cited by 129
  6. 2023Future Lens: Anticipating Subsequent Tokens from a Single Hidden Statecited by 121
  7. 2024Aligning LLM Agents by Learning Latent Preference from User Editscited by 69
  8. 2026Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thoughtcited by 40
  9. 2025Emergent Response Planning in LLMscited by 19
  10. 2026In Agents We Trust, but Who Do Agents Trust? Latent Source Preferences Steer LLM Generationscited by 6
  11. 2026Early Stopping for Large Reasoning Models via Confidence Dynamicscited by 6
  12. 2025Aligning Large Language Model Agents with Rational and Moral Preferences: A Supervised Fine-Tuning Approachcited by 5
  13. 2026Understanding and Mitigating Premature Confidence for Better LLM Reasoningcited by 5
  14. 2026Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Modelscited by 5
  15. 2026Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Modelscited by 3
  16. 2026A Behavioural and Representational Evaluation of Goal-Directedness in Language Model Agentscited by 1
  17. 2026Answer-Distribution Trajectories: A Stochastic-Dynamics View of LLM Reasoningcited by 1

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