What Attention Recalls and Recurrence Controls in Hybrid Language Models

Abstract:Hybrid language models combine attention with a fixed-size recurrent state, but the role of each channel remains unclear. We introduce two cache-level interventions. Split-prefill keeps only the KV cache or only the recurrent state from a prefilled context, then generates an answer. State-swap pairs the KV cache from one context with the recurrent state from another in a single forward pass. On Qwen3.5 and Falcon-H1, the two channels split sharply by function. Exact retrieval survives only through attention (64-98% of full accuracy) and collapses to zero through recurrence. Output language and persona reverse the pattern: both survive recurrence (70-80% and 3-5x) while KV-only drops to ~1% language accuracy. State-swap confirms this causally: the answer takes its value from the KV side and its language from the recurrent side. Recurrent-only generation also accepts words that were never in the context but share meaning or parts with seen items. Attention provides a lookup over what was said; the recurrent state shapes how the model says it next.

Accepted to Findings of EMNLP 2026. 13 pages, 3 figures, 8 tables. Code: this https URL

Computation and Language (cs.CL)

arXiv:2609.04434 [cs.CL]

(or arXiv:2609.04434v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.04434 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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