Lagged Coupling: Internal Representations Become Readable Before They Become Causal
On the map: Mechanistic interpretability → Circuits and mechanistic analysis
Abstract (arXiv)
Across the full Pythia suite (160M-12B, eight checkpoints, four task families), a linear probe can read a target variable from the residual stream as early as step 1,000 at every scale -- yet steering along that same reading direction remains null-equivalent in 43 of 48 model-checkpoint cells. Internal readability systematically outruns causal efficacy, and the lag does not shrink with scale. We call this structure lagged coupling and decompose it into three dissociable tracks: (i) internal readability, saturated (AUROC >= 0.990) from the first checkpoint everywhere; (ii) behavioral readability, which develops gradually and progressively later at larger scales (12B reaches 0.909 only at the final checkpoint); (iii) causal efficacy, almost always null-equivalent, occasionally counterproductive early, with one isolated positive pulse (12B, step 8,000, z = +2.49) our grid cannot resolve. The ordering is dominantly read-before-write (11/11 units, no inversion). Representation headroom along the probe direction grows up to 57x with training and scale while causal write-in stays below 0.11% of headroom -- the variable is increasingly written into the representation and increasingly ignored by the readout. Under a fully pre-registered protocol, both single-onset hypotheses resolve INDETERMINATE (scale slope +0.24, 95% CI [-0.60, +0.87]; time vote 3:3) -- a disciplined negative explained by the three-track decomposition. A pre-registered OLMo-2 replication preserves the direction at attenuated magnitude. Our results caution against inferring steerability from probe accuracy and establish a developmental bottleneck: representation formation reliably outpaces causal readout consolidation.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Circuits and mechanistic analysis, one of 13 directions in Mechanistic interpretability. Builds on 22 of its 30 references that are in the map. First posted on arXiv 1 Sep 2026.
Builds on
22 of 30 references.
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Same direction, newest first
- 2023Progress measures for grokking via mechanistic interpretability12 Jan
- 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small1 Nov
Other references · 20
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- 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 876
- 2019Designing and Interpreting Probes with Control Taskscited by 640
- 2022Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasetscited by 629
- 2023Are Emergent Abilities of Large Language Models a Mirage?cited by 523
8 more
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- 2021Inducing Causal Structure for Interpretable Neural Networkscited by 110
- 2022Probing for the Usage of Grammatical Numbercited by 59
- 2022Probing Classifiers are Unreliable for Concept Removal and Detectioncited by 41
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