WnW: Waxing-and-Waning KV Cache for Long-Form Speech LLMs

Abstract:Long-form audio inputs make the KV cache the dominant memory cost of speech LLMs. Prefill-only KV compression methods permanently discard audio KV positions once evicted, with no pathway to recover them during decoding. We show this is fragile on long-form audio: prefill attention concentrates near the audio start (an attention-sink effect), while decode-time attention distributes broadly, and the two rankings overlap weakly. We propose WnW (Waxing-and-Waning KV cache), which classifies KV-heads into anchor, tidal, and fixed roles via offline calibration. Anchor heads remain on GPU and serve as a decode-time importance observer; tidal heads keep a CPU-resident complement that is recalled chunk-by-chunk based on aggregated anchor-head scores; fixed heads keep only an on-GPU subset, with the rest permanently discarded. On LibriSpeech-Long with two 3B backbones (Voxtral-mini-3b and Qwen2.5-Omni-3B), WnW preserves near-Full-Cache accuracy while keeping only 20% of audio tokens on GPU, where prefill-only baselines fail to terminate. Results generalize across language, task, and domain shifts, and CPU-GPU recall adds little decode-time overhead in our measurements.

Accepted at EMNLP 2026 Main Conference. 8 pages, 5 figures

Computation and Language (cs.CL); Sound (cs.SD)

arXiv:2608.22704 [cs.CL]

(or arXiv:2608.22704v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.22704 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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