MIT发现AI模型内部表征可读性先于因果性，这一滞后现象在12B参数规模下依然存在，挑战了传统模型理解。
研究显示在Pythia模型套件(160M-12B参数)中，线性探针可在第1000步读取目标变量，但沿相同方向的引导在48个模型检查点中有43个无效。内部可读性系统性地领先于因果效力，且这种滞后不会随规模缩小。研究将此结构分解为三个可分离的轨迹：内部可读性在第一个检查点就已饱和(AUROC≥0.990)，行为可读性随规模逐渐发展且出现更晚(12B模型仅在最后检查点达到0.909)，因果效力几乎总是无效。
Lagged Coupling: Internal Representations Become Readable Before They Become Causal
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.