AgentKVShift cuts agentic memory prefill latency 2-3.5x on A100
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AgentKVShift gets near full-recompute quality for agentic memory retrieval while refreshing only 10–30% of KV cache, producing 2–3.5x prefill speedups on a single A100 versus no KV reuse. The key production implication is that structured, metadata-heavy agent memories can be cached and reused without the quality collapse seen in RAG-oriented KV reuse, including under 2- and 4-bit KV quantization.
LLMs using agentic memory systems can now reuse up to 70-90% of their Key-Value (KV) cache with AgentKVShift, a training-free method that corrects reused tokens with a weighted correction, achieving near full recompute performance. This enables 2-3.5x prefill speedups on a single A100, significantly reducing inference latency for long-horizon applications. This directly impacts production LLM deployments, allowing for faster and more efficient processing of complex agentic memory tasks.