English(EN) On the Expressive Power and Limitations of Multi-Layer SSMs

新研究探讨多层SSM的表达能力和局限性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

一篇新论文探讨了多层状态空间模型（SSM）的表达能力和局限性。研究人员分析了深度、精度、状态维度和思维链（CoT）推理等因素对这些模型的影响。该研究为解决特定的顺序信息传播问题提供了理论界限，并区分了后输入推理和输入交错推理。 AI

影响为状态空间模型的性能和限制提供了理论见解，可能影响未来的架构设计。

排序理由该集群包含一篇详细阐述多层状态空间模型理论分析的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 22 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细阐述多层状态空间模型理论分析的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

arXiv cs.AI TIER_1 English(EN) · Nikola Zubi\'c, Qian Li, Yuyi Wang, Davide Scaramuzza · 2026-09-03 04:00

多层SSM的表达能力与局限性

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…