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New research probes multi-layer SSMs' expressive power and limitations

By PulseAugur Editorial · [1 sources] ·

A new paper explores the expressive power and limitations of multi-layer state-space models (SSMs). Researchers analyzed how factors like depth, precision, state dimension, and chain-of-thought (CoT) reasoning impact these models. The study provides theoretical bounds for solving specific sequential information propagation problems and distinguishes between post-input and input-interleaved reasoning. AI

IMPACT Provides theoretical insights into the capabilities and constraints of state-space models, potentially influencing future architectural designs.

RANK_REASON The cluster contains a research paper detailing theoretical analysis of multi-layer state-space models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research probes multi-layer SSMs' expressive power and limitations

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The cluster contains a research paper detailing theoretical analysis of multi-layer state-space models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nikola Zubi\'c, Qian Li, Yuyi Wang, Davide Scaramuzza ·

    On the Expressive Power and Limitations of Multi-Layer SSMs

    arXiv:2604.14501v2 Announce Type: replace-cross Abstract: We study how depth, finite precision, state dimension, and chain-of-thought (CoT) affect the expressive power of multi-layer state-space models (SSMs). For the explicit-table $K$-function-composition problem, a canonical b…

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