An Exact Instrument for State Usage in Selective State-Space Models, and the Input-Driven Migration It Reveals
Score
78首次提出精确测量选择性状态空间模型中状态使用的方法，并揭示输入驱动迁移机制，信息增量大。
理论严谨，实验覆盖多个模型家族且验证精度极高，归因分析有深度。
提出的剪枝策略在多个规模上优于现有方法，且在半预算下保持性能，对实际部署有启发。
发布于最新arXiv，针对当前热门的状态空间模型研究，时效性强。
来自arXiv预印本，实验细节充分，代码可复现，信源可信。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper presents a novel and exact measurement method with thorough experimental validation and insightful attribution analysis, contributing significantly to model understanding and optimization.
AI commentary
「This paper is highly recommended for researchers studying inference efficiency and internal mechanisms of state-space models like Mamba. It provides the first exact measurement of mode usage and reveals the important phenomenon of input-driven migration, deepening our understanding of selection mechanisms while offering practical pruning strategies. The result of matching unpruned performance at half the state budget is especially attractive for deployment engineers.」
Quick overview
This paper presents an exact instrument for measuring how trained selective state-space models (such as Mamba) use their state modes. Thanks to the diagonal state matrix, each channel's output decomposes exactly into per-mode contributions, and a Gram tensor enables offline computation of the output error when dropping any subset of modes. Validated on Mamba-1 with relative error 2.3×10⁻⁷, the instrument predicts pruning error with median relative deviation 5×10⁻⁷ across 4,464 configurations. Applied to Mamba-1, Falcon-Mamba 7B, and Mamba-2, it reveals that models reallocate state space dynamically based on input: the modes carrying the signal migrate across contexts, and at the most affected layers a per-input oracle roughly halves the output error of a fixed mode set. Counterfactual analysis attributes migration primarily to the input-dependent write map B_t, not the timestep. Input-scheduled mode pruning based on this measurement outperforms static, Hankel-based, and layer-adaptive rankings at all scales from 130M to 7B, and at half the state budget matches the unpruned model.
- An exact instrument for measuring state mode usage is proposed, leveraging diagonal state matrix for output decomposition.
- Input-driven state migration is verified across Mamba-1, Mamba-2, and Falcon-Mamba series.
- Counterfactual analysis attributes migration primarily to the input-dependent write map B_t, not the timestep.
- Input-scheduled pruning based on this measurement matches unpruned model performance at half the state budget.
Full text
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