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Mamba models struggle with retrieval due to delayed relevance signals, not just state capacity

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper challenges the assumption that Mamba models underperform Transformers in in-context retrieval due to state capacity limitations. Researchers found that even when the query precedes the context, Mamba models lag behind Transformers. This is because the relevance signal for retrieval only emerges gradually with model depth, and Mamba's selection gates do not align with this signal until several layers deeper, thus hindering successful retrieval beyond state capacity issues. AI

IMPACT Highlights a potential bottleneck in Mamba's architecture for retrieval tasks, suggesting further research is needed for competitive performance.

RANK_REASON Research paper analyzing model performance and limitations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

Mamba models struggle with retrieval due to delayed relevance signals, not just state capacity

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Research paper analyzing model performance and limitations. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Nir Yarden, Amit Ben-Artzy, Roy Schwartz ·

    Beyond State Capacity: Why Retrieval Breaks in Mamba

    arXiv:2610.19709v1 Announce Type: new Abstract: Mamba models underperform Transformers on in-context retrieval. This gap is usually attributed to Mamba's fixed-size recurrent state, which loses information as the context grows. We challenge this assumption by focusing on query-fi…

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