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Zhejiang University
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TokenPilot slashes inference costs by up to 87% without sacrificing performance, tackling the critical trade-off between context management and cache efficiency in LLM agents.
LLM memory failures are systematic, stemming from operation-level issues like information loss and retrieval misalignment, and can be automatically corrected with prompt optimization guided by fine-grained error tracing.
LLM agents can achieve state-of-the-art performance in dynamic environments by treating memory as a continuously evolving graph, rather than a static repository.
