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This research addresses a critical reliability issue in long-running AI agents where cache-history sensitivity causes identical prompts to yield different answers based on preceding request orders. By introducing document-aligned recomputation strategies, the study significantly reduces answer variation and improves fidelity compared to standard token top-$k$ policies while maintaining substantial speedups. The findings highlight that spatial contiguity is a key factor for robust selective KV-cache reuse in dynamic agent workloads.
Request Order Matters: Cache-History Sensitivity in Selective KV-Cache Reuse for Rolling Agents
arXiv:2610.05833v1 Announce Type: new Abstract: Long-running agents repeatedly call an LLM while retaining most of their document window, evicting old documents, and appending new ones. These rolling updates break exact prefix caching and motivate non-prefix KV-cache reuse with selective recomputation. We show that persistent KV-cache reuse with selective recomputation can be history-dependent: in our rolling-agent workload, an unchanged prompt can produce different answers depending on the requests processed before it. At a matched 5% recomputation budget, document-aligned recomputation reduces answer variation across request orders from 69.0% with CacheBlend's token top-$k$ policy to 26.1%. When each prompt is evaluated after a different sequence of preceding requests, document-aligned recomputation improves fidelity to full prefill by 34.5-52.5 percentage points over token top-$k$, while both policies achieve approximately 5.7$\times$ median TTFT speedup. Our ablation study shows that, in our rolling-agent workload, contiguity is the main factor associated with robust selective recomputation.
Abstract:Long-running agents repeatedly call an LLM while retaining most of their document window, evicting old documents, and appending new ones. These rolling updates break exact prefix caching and motivate non-prefix KV-cache reuse with selective recomputation. We show that persistent KV-cache reuse with selective recomputation can be history-dependent: in our rolling-agent workload, an unchanged prompt can produce different answers depending on the requests processed before it. At a matched 5% recomputation budget, document-aligned recomputation reduces answer variation across request orders from 69.0% with CacheBlend's token top-$k$ policy to 26.1%. When each prompt is evaluated after a different sequence of preceding requests, document-aligned recomputation improves fidelity to full prefill by 34.5-52.5 percentage points over token top-$k$, while both policies achieve approximately 5.7$\times$ median TTFT speedup. Our ablation study shows that, in our rolling-agent workload, contiguity is the main factor associated with robust selective recomputation.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Performance (cs.PF)
arXiv:2610.05833 [cs.AI]
(or arXiv:2610.05833v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.05833 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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