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This work proposes ActKV, the first KV cache compression framework tailored for agentic LLM inference, and establishes a compression criterion that values KV entries by their contribution to action generation and prioritizes action quality.
This work is the first to recast expert activation prediction as sequence modeling, enabling accurate multi-step, multi-layer forecasts that provide a long and reliable window for downstream decisions, and derives general runtime principles encompassing compute-transparent expert placement and synchronization-free orch...
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