Beyond Causal Masks: Auditing and Localizing Hidden Future-Token Leakage in Adaptive Sequence Models
Autoregressive sequence models can suffer hidden future-token leakage when auxiliary operations—such as pooling, routing, or normalization—broadcast full-sequence summaries outside the causal attention mask. This paper audits these violations through prefix-invariance checks, a LeakScore metric, and layer-wise FirstLeakLayer localization, demonstrating that such leakage can produce false architectural gains: a CDR-AttnRes checkpoint drops teacher-forced validation loss from roughly 3.6 to 1.43 while its free-running generation perplexity explodes from under 20 to about 700. The core lesson is that teacher-forced validation loss must be paired with whole-model causal audits and open-ended generation diagnostics before claiming progress.
The paper makes a practically important point: hidden future-token access outside masked attention can create illusory gains that vanish under causal repair. The 24-layer CDR-AttnRes case and the 12-layer repair comparisons are carefully executed, but the scope is limited to a single architectural family from the authors' own follow-up work, and the observed dose-response between leakage magnitude and loss gain is statistically weak within the leaking group. It is best read as a cautionary case study and a usable diagnostic recipe rather than a broad empirical survey.
The empirical core is strong. The anomalous 24-layer checkpoint is extreme—validation loss falls to 1.43 while reference perplexity during generation balloons to roughly 700—and two independently trained 12-layer causal repairs simultaneously eliminate detectable leakage and return both validation loss and generation quality to the control scale. Synthetic module pairs and fault injection on GPT-2, Qwen, and Mamba further show that the audit cleanly separates non-causal from causal implementations.
The central limitation is generalizability. The authors correctly note that the CDR-AttnRes case study is 'still a single concrete case from our own architectural exploration,' and within the four leaking variants the association between leakage magnitude and apparent gain is weaker and not statistically significant ($r=-0.559$, $p=0.441$). Additionally, no 24-layer causal repair is trained, leaving open whether the false gain and its elimination scale with depth. Finally, prefix invariance and max discrepancy are established prior contracts, so the paper's novelty resides mainly in the case documentation and localization protocol rather than in the audit definition itself.
The evidence tightly supports the claim that auxiliary paths can leak: all six non-causal synthetic variants fail prefix invariance while their causal repairs score exactly zero, and fault injection on three public models fails universally with high affected-token rates. Yet the paper does not run prior causal auditors such as FBS or TorchLean on the same checkpoint, making comparative benchmarking impossible. The synthetic modules are also untrained, so their import for real-world architectures rests heavily on the single trained case study.
Experimental protocols are specified with unusual care: the audit uses $T=64$, prefix lengths in $\{16,32,48\}$, three seeds, eight suffix perturbations per setting, and a tolerance of $\epsilon=10^{-5}$. Training hyperparameters for CDR are fully tabulated. However, model checkpoints are omitted due to storage constraints, the authors explicitly disclaim that 'exact numerical agreement across environments is not guaranteed,' and no 24-layer causal repair checkpoint is provided, so the core disappearing-gain narrative cannot be fully reproduced by others.
Hidden future-token leakage can create false architectural gains: an autoregressive model may appear substantially better under teacher-forced evaluation while its free-running generation deteriorates. We investigate this failure in an adaptive residual-routing decoder whose sequence-level routing summary exposes prefix positions to future tokens despite a correct causal attention mask. An anomalous 24-layer checkpoint reduces validation loss from the baseline's 3.5958 to 1.4259, yet it fails a prefix-invariance audit and has substantially higher reference-model perplexity during generation. Layer-wise diagnostics and implementation inspection trace the violation to a full-sequence summary broadcast through the dynamic routing-query path. Two independently trained 12-layer causal repairs eliminate detectable leakage and return validation loss and reference perplexity to the control scale, removing the large apparent advantage. As diagnostic tools, we use the established prefix-invariance contract, maximum prefix-output discrepancy (LeakScore), complementary behavioral metrics, and layer-wise profiles. Supporting experiments cover six synthetic implementation pairs spanning five module families, clean pretrained controls, controlled fault injection, and cache consistency across numerical precisions. These results illustrate how hidden future-token access can produce misleading claims of architectural progress and why lower teacher-forced loss should be interpreted together with whole-model causal checks and free-running evaluation.
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