MiniCache: Reusable Program Caching with Small Model Interfaces for Efficient LLM Inference

תקציר מקורי באנגליתarXiv:2607.20507v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for program-aided reasoning, agentic decision making, and structured task execution, but these applications often incur high inference cost. We present MiniCache, a reusable program caching framework that transforms Program-of-Thought (PoT) programs into parameterized cache objects, enabling reusable computation across structurally similar requests. MiniCache reuses the same small model for semantic variable extraction on cache-hit requests and speculative drafting during target-LLM generation, reducing expensive target-LLM invocations while preserving task quality. Experiments on shopping-style request datasets, WebShop, Formula, and CodeTAT-QA demonstrate that MiniCache improves the trade-o