Polynomial Context-Truncation Sensitivity in Autoregressive Language Models: Sequential Wyner-Ziv Bounds for KV Cache Compression
arXiv:2605.25085v1 Announce Type: cross Abstract: We study the rate-distortion limits of online KV cache compression in autoregressive language models, formulating it as sequential Wyner-Ziv source coding on the filtration induced by the model, with the next-step query as decoder side information. Empirically, across four models spanning two families and $0.5$-$3$B parameters, we find that the next-token distribution's sensitivity to context truncation decays \emph{polynomially} rather than \emph{geometrically}: a power law improves on an exponential fit by an order of magnitude in extrapolati
This research addresses a fundamental efficiency challenge in large language models, driven by the increasing scale and computational demands of these systems.
Improving KV cache compression directly impacts the operational cost and scalability of large AI models, influencing widespread adoption and accessibility.
The understanding that context sensitivity decays polynomially rather than geometrically shifts the theoretical basis for optimizing memory and inference for autoregressive language models.
- · AI model developers
- · Cloud providers
- · AI inference providers
- · Inefficient AI architectures
- · High-cost AI inference solutions
More efficient and cost-effective deployment of large language models, enabling broader use cases.
Accelerated development of even larger and more complex AI models due to relaxed memory constraints.
Potentially democratizes access to advanced AI capabilities by lowering computational barriers.
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Read at arXiv cs.LG