mlsanityData to 14 Oct 2026

When and How Long? The Readout-Mediator Angle in Temporal Reasoning

arXiv 2605.29126 ↗First posted 27 May 2026cs.LG cs.AIcited by 1

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

also in 1 more
Abstract (arXiv)

A linear probe can decode a representation almost perfectly and yet be completely irrelevant to how the model uses it. On calendar-date duration reasoning in language models, a sin/cos probe recovers day-of-year from a layer's activations, yet ablating its direction has no effect on the model's answers -- while ablating a four-dimensional subspace found by Distributed Alignment Search (DAS) at the same layer collapses performance entirely. We measure the angle between these two subspaces -- the readout-mediator angle -- and find it indistinguishable from the angle between two random subspaces (the Haar-uniform null), meaning the probe has learned a direction orthogonal to the model's actual computation. Reverse-engineering the circuit reveals why: attention heads route month-grained context through learned QK offsets at ±30 and ±61 days, and MLPs then convert when (absolute date) into how long (duration) -- all downstream of the causal subspace the probe never touches. Sparse-autoencoder decomposition confirms the split: probe-aligned and DAS-aligned features encode semantically disjoint concepts with negligible causal overlap. The dissociation replicates across four scales (1.5-9,B) and two model families, with preliminary evidence on two further domains (spatial displacement, symbolic arithmetic), suggesting that readout-mediator orthogonality is a general failure mode of probe-based interpretability. This directly undermines proposals to deploy probes as runtime safety monitors: the probe can report high confidence on a direction the model has silently abandoned.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 9 of its 45 references that are in the map. First posted on arXiv 27 May 2026.

Builds on

9 of 45 references.

References · 9

  1. 2024Qwen2.5 Technical Reportcited by 4,942
  2. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,998
  3. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,336
  4. 2024Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2cited by 394
  5. 2025Language Models Use Trigonometry to Do Additioncited by 70
  6. 2025The Origins of Representation Manifolds in Large Language Modelscited by 51
  7. 2026When Models Manipulate Manifolds: The Geometry of a Counting Taskcited by 46
  8. 2025Dimensional Collapse in Transformer Attention Outputs: A Challenge for Sparse Dictionary Learningcited by 6
  9. 2025Seed-Induced Uniqueness in Transformer Models: Subspace Alignment Governs Subliminal Transfercited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2025Date Fragments: A Hidden Bottleneck of Tokenization for Temporal Reasoning
  2. 2026What Really Controls Temporal Reasoning in Large Language Models: Tokenisation or Representation of Time?
  3. 2026TIME: Temporally Intelligent Meta-reasoning Engine for Context-Triggered Explicit Reasoning
  4. 2026Measuring Iterative Temporal Reasoning with Time Puzzles
  5. 2025ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events
  6. 2026Linear Probes Detect Task Format, Not Reasoning Mode in Language Model Hidden States
  7. 2025Discrete Minds in a Continuous World: Do Language Models Know Time Passes?
  8. 2024Timo: Towards Better Temporal Reasoning for Language Models