mlsanityData to 14 Oct 2026

Belief-reality separation lives in routing over a shared value slot in language models

arXiv 2607.11945 ↗First posted 11 Jul 2026cs.CL cs.LGcited by 0

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Abstract (arXiv)

Capable language models hold what a character believes apart from what is true: told "Anna believes the cup is blue; in reality it is red," they answer blue about Anna and red about the world. Where in the computation does that separation live? We show it rests on two separable mechanisms at two positions. A generic value slot binds the attributed value. A router at the query position selects which frame, the character's belief or reality, a query reads out. Two routes fill the slot: an asserted belief, whose value the text supplies, binds in directly; a derived belief, whose value must be inferred from what the character could see, arrives by a visibility-gated lookback. A subspace trained on either route steers the other, and only the derived route depends on described visibility. The slot itself carries no belief-reality tag: intervening on it moves a reality readout as strongly as a belief one. The separation lives instead in a dissociated pair of routing subspaces, which flip a query between frames without injecting the donor's value. These results hold across three architectures, on stimuli de-confounded against theory-of-mind-benchmark shortcuts; the behavior itself emerges between 3B and 7B across five model families. This paper develops the single belief-reality axis in depth; a companion paper shows the same slot-and-router format is shared across the other non-actual contexts a sentence can open (counterfactual, fictional, temporal).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 28 references that are in the map. First posted on arXiv 11 Jul 2026.

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References · 23

  1. 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 767
  2. 2023The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasetscited by 712
  3. 2023Large Language Models Fail on Trivial Alterations to Theory-of-Mind Taskscited by 292
  4. 2024Retrieval Head Mechanistically Explains Long-Context Factualitycited by 232
  5. 2023Evaluating Large Language Models in Theory of Mind Taskscited by 222
  6. 2022Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMscited by 220
  7. 2023Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representationscited by 209
  8. 2023Understanding Social Reasoning in Language Models with Language Modelscited by 172
  9. 2023FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactionscited by 171
  10. 2023Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Modelscited by 163
  11. 2024Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Trackingcited by 149
  12. 2023How do Language Models Bind Entities in Context?cited by 108
11 more
  1. 2023Interpretability at Scale: Identifying Causal Mechanisms in Alpacacited by 103
  2. 2023Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Trackercited by 95
  3. 2023Still No Lie Detector for Language Models: Probing Empirical and Conceptual Roadblockscited by 78
  4. 2023HI-TOM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Modelscited by 63
  5. 2025Language Models use Lookbacks to Track Beliefscited by 39
  6. 2024Position: Theory of Mind Benchmarks are Broken for Large Language Modelscited by 31
  7. 2024Standards for Belief Representations in LLMscited by 30
  8. 2024Representational Analysis of Binding in Language Modelscited by 28
  9. 2024Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Modelscited by 22
  10. 2025How Do Transformers Learn Variable Binding in Symbolic Programs?cited by 21
  11. 2026Language Statistics and False Belief Reasoning: Evidence from 41 Open-Weight LMscited by 3

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