When and How Long? The Readout-Mediator Angle in Temporal Reasoning
On the map: LLM reasoning → Knowledge- and tool-augmented reasoning
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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
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Citing papers per year: 2026: 1.
1 paper.
- 2026Decodable In-Context State and Model Output Across Training25 Sep 2026
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- 2026TIME: Temporally Intelligent Meta-reasoning Engine for Context-Triggered Explicit Reasoning
- 2026Measuring Iterative Temporal Reasoning with Time Puzzles
- 2025ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events
- 2026Linear Probes Detect Task Format, Not Reasoning Mode in Language Model Hidden States
- 2025Discrete Minds in a Continuous World: Do Language Models Know Time Passes?
- 2024Timo: Towards Better Temporal Reasoning for Language Models