Introduces BeliefTrack benchmark diagnosing three CBM failures in LLMs and shows RL with belief-state rewards cuts failure rates by 70.9% while representation steering cuts them by 46.1%.
Indications of belief-guided agency and meta-cognitive monitoring in large language models.arXiv preprint arXiv:2602.02467, 2026
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
Re-examination of two LLM introspection paradigms with new controls shows models lack privileged access to internal states, performing equivalently with input-only classifiers or near chance on relabeled tasks.
Agents in a minimal multi-agent RL setup develop self-referential communication and an echo-mismatch detection circuit that emerges from environmental affordances rather than task structure or architecture.
citing papers explorer
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When Should Models Change Their Minds? Contextual Belief Management in Large Language Models
Introduces BeliefTrack benchmark diagnosing three CBM failures in LLMs and shows RL with belief-state rewards cuts failure rates by 70.9% while representation steering cuts them by 46.1%.
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Can LLMs Introspect? A Reality Check
Re-examination of two LLM introspection paradigms with new controls shows models lack privileged access to internal states, performing equivalently with input-only classifiers or near chance on relabeled tasks.
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Emergent Language as an Approach to Conscious AI
Agents in a minimal multi-agent RL setup develop self-referential communication and an echo-mismatch detection circuit that emerges from environmental affordances rather than task structure or architecture.