ReCache makes KV cache reusable for tool-heavy LLM agents

Agent workloads repeatedly encode tool and skill schemas that recur across requests in different combinations and orders, so standard prefix caching cannot reuse their KV states. ReCache caches each resource's representation independently: resource-wise attention removes cross-resource interactions and assigns resource-local positions, producing composition-invariant KV blocks; visibility is restricted to contribution-selected layer-KV-head-group routes, and structural plus semantic pruning keeps only invocation-critical fields. On a benchmark assembled from seven public tool/skill-use datasets (including resource-disjoint tests), resource-wise attention matches dense invocation performance (82.3% vs 82.4% Inv-F1) while delivering a 3.655x time-to-first-token speedup; the full framework cuts allocated KV-tensor memory by 92.43% and accelerates attention by 1.423x. Code is available at github.com/EIT-NLP/ReCache.

Prefix caching fails exactly where agent traffic is heaviest: tool schemas recombined on every request. For teams running MCP or tool-heavy gateways, this is a concrete serving-cost lever on both TTFT and KV memory, complementing the correctness side of agent x KV-cache interactions (ev-20260818-03). The caveats: it requires attention-level changes (resource-wise attention), so it is not a drop-in cache configuration; integration into production serving stacks carries real cost, and the evaluation is academic-benchmark scale.