mlsanityData to 14 Oct 2026

When Does Routing Become Interpretable? Causal Probes on Block Attention Residuals

arXiv 2606.13168 ↗First posted 11 Jun 2026cs.LGcited by 0

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Abstract (arXiv)

Block Attention Residuals (Block AttnRes) by replace fixed additive residuals with a learned softmax over earlier depth-source representations, surfacing cross-layer routing as an inspectable tensor in the forward pass. This is a tempting interpretability target: information flow normally inferred indirectly is now directly observable. We ask whether such exposure suffices for mechanistic interpretation. We probe two same-scale (0.6B) Block AttnRes checkpoints under identical routing-ablation interventions: a vanilla Qwen3 inference-wrapped through a deterministic recency-bias schedule that the codebase admits as a routing-equivalent loading path, and a Block AttnRes Qwen3 trained from scratch with routing as part of optimisation. The wrapped baseline's routing weights are content-independent and reproduce the schedule's analytic prediction. The trained AttnRes checkpoint instead exhibits three localised routing motifs: an embedding-source pathway through early-layer MLP, a current-state pathway through early-layer attention and MLP, and an older-history pathway through late-layer attention. Beyond this stratification, we find a sharp dissociation between average routing mass and causal importance: in both sublayers, the largest mass slice is not the largest causal contribution, and one source family carries appreciable mass with no detectable causal role under intervention. Architectural exposure of routing is therefore necessary but not sufficient for mechanistic interpretation: structured depth routing emerges only when routing has been part of training, and even then, descriptive routing summaries should be treated as candidate hypotheses to be tested by causal interventions, not as evidence of mechanism in their own right.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 5 references that are in the map. First posted on arXiv 11 Jun 2026.

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References · 5

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2022Locating and Editing Factual Associations in GPTcited by 2,691
  3. 2020On Layer Normalization in the Transformer Architecturecited by 922
  4. 2024A Practical Review of Mechanistic Interpretability for Transformer-Based Language Modelscited by 118
  5. 2026SiameseNorm: Breaking the Barrier to Reconciling Pre/Post-Normcited by 10

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