arXiv — NLP / Computation & Language · · 3 min read

Detection vs. Execution: Single-Bucket Probes Miss Half the Mamba-2 State Sink

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Computer Science > Computation and Language

arXiv:2606.00930 (cs)
[Submitted on 30 May 2026]

Title:Detection vs. Execution: Single-Bucket Probes Miss Half the Mamba-2 State Sink

Authors:Yuhang Jiang
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Abstract:Mechanistic interpretability often assumes that probes identifying a representational signature also identify the circuit executing the corresponding computation. We show that this assumption can fail systematically in Mamba-2. Studying the state sink (disproportionate Delta-gate activation on boundary tokens, analogous to the attention sink), we find that single-bucket probes recover only a small execution layer while missing a much larger detection layer with the same representational signature.
In Mamba-2, the state sink decomposes into two functional head sets. Single-bucket BOS-specialist heads (about 5% of heads at 2.7B) causally support both BOS-context and newline-target predictions across model scales and corpora. Dual heads (27-35% of heads, recovered by multi-class aggregation of the same probe) show stronger BOS-newline representational similarity but substantially weaker causal effects under ablation. Representational similarity does not imply functional equivalence.
This distinction matters for downstream behaviour: ablating BOS-specialist heads collapses RULER NIAH retrieval accuracy from 1.00 to 0.00 at 1024 context length in both Mamba-1 2.8B and Mamba-2 2.7B, while size-matched complements preserve baseline performance. A random channel-bucketing control rules out substrate granularity alone, implicating Mamba-2's head-shared Delta projection. Probe-derived specialty can identify execution circuits; at coarse granularity the same probe also recovers detection circuits, and separating them requires class-conditional ablation rather than class-conditional cosine.
Comments: 16 pages, 3 figures
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2606.00930 [cs.CL]
  (or arXiv:2606.00930v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2606.00930
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yuhang Jiang [view email]
[v1] Sat, 30 May 2026 23:37:01 UTC (69 KB)
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