mlsanityData to 7 Oct 2026

A Calibrated Test of Internal Action Maps: State Signals Without Global Affine Closure

arXiv 2608.13626 ↗First posted 13 Aug 2026cs.AI cs.CL cs.LGcited by 0

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Abstract (arXiv)

A hidden state signal can be decodable or causally usable without supporting a reusable action map. We test whether action maps fitted without a source reach its natural post-action activation and compose. We organize the tests as an evidence lattice and validate the geometric branch on a known affine S_5 carrier: all held-source folds pass one-step, composition, inverse, decoding, and commutativity gates. Structured curvature and held-domain conjugacy raise error monotonically, but only 23/30 strongest cells flip a closure gate, bounding rather than universalizing calibration. In post-trained Qwen/Qwen3-4B, frozen final-token h28 affine maps have mean held-entity error .519, versus .398 for within-test-domain cross-fit. Seven randomized entity splits and map geometry do not support a purely entity-specific account. Earlier h4/h16 layers fit one-step transitions better, but h4 conflict-state decoding is weak and lexical controls remain unresolved. Three matched intervention datasets regenerated from one frozen checkpoint show causal effects only at h28/h36. Outcome-aware refitting improves h28 one-step error to .474 (.469 with weighting), yet no refit passes composition. Learned finite worlds likewise preserve relative algebraic signals or shared charts without held-source affine closure. Within the tested carriers, state availability, causal use, local geometry, and reusable closure are separable. The result is limited to one pretrained model, sampled final-token layers, two finite worlds, and the tested affine or diagnostic function classes.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 16 references that are in the map. First posted on arXiv 13 Aug 2026.

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References · 15

  1. 2024Training Large Language Models to Reason in a Continuous Latent Spacecited by 763
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  3. 2023In-Context Learning Creates Task Vectorscited by 302
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  8. 2024Evaluating the World Model Implicit in a Generative Modelcited by 107
  9. 2017Simulating Action Dynamics with Neural Process Networkscited by 53
  10. 2025Language Models use Lookbacks to Track Beliefscited by 36
  11. 2025(How) Do Language Models Track State?cited by 31
  12. 2025How Do Language Models Compose Functions?cited by 7
3 more
  1. 2025AlgoTrace: Algorithmic Primitives and Compositional Geometry of Reasoning in Language Modelscited by 7
  2. 2026Representational Homomorphism Predicts and Improves Compositional Generalization In Transformer Language Modelcited by 2
  3. 2026A Held-Out Transition-Pair Falsifier for Long-Horizon Non-Abelian State Trackingcited by 2

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