Researchers have introduced ReCache, a framework aimed at reducing repeated inference work in tool-using language agents. The problem is specific but important: agents often encode the same tool and skill schemas across requests, yet standard prefix caching cannot easily reuse those representations when tools appear in different combinations or orders.
ReCache separates resource representations, uses resource-wise attention, and prunes stored information so only invocation-critical fields remain visible. In the reported benchmark, resource-wise attention matched dense invocation performance closely while improving time to first token, and the full framework sharply reduced allocated key-value cache memory.
The results are from a research benchmark, not a broad production rollout. Still, they point to a practical path for making agent systems cheaper and faster as tool catalogs grow.