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2 results for “multi-turn agents”
CommitKV: Lifecycle-Aware KV Cache Compression via Commit Transitions for Multi-Turn Agents
CommitKV is a new KV cache compression method for multi-turn ReAct agents that identifies and removes only truly completed information—distinguishing it from temporarily dormant but future-relevant data—thereby reducing memory use, speeding inference, and improving accuracy.
Aug 11, 2026
When Privileged Guidance Misaligns: State-Matched Routing and Contextualized Self-Distillation for Multi-Turn Agents
A new AI training method called SMRC-SD improves multi-turn agent performance by selectively applying privileged teacher guidance only when the student’s current execution state matches supported states in reference trajectories, increasing task success rates on ALFWorld and WebShop benchmarks.
Aug 7, 2026