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arXiv Computation and Language By Matthew Ho, Chenglong Wang, Baolin Peng, Nebojsa Jojic, Philippe Laban, Michel Galley, Jianfeng Gao, Lianhui Qin

Agentic Context Management for Multi-Turn Human-AI Conversations

Read the original on arXiv Computation and Language →

The paper introduces Agentic Conversation Context Curation (AC3), a training‑free method that cleans multi‑turn human‑AI dialogue by consolidating the user’s task specification, auditing assistant outputs, and editing the context to remove or rebuild assistant turns. AC3 outperforms both full context and assistant‑omission (AO) baselines on several benchmark tasks, closing a significant portion of the performance gap and receiving preference from a GPT‑5‑mini judge on real WildChat conversations. The study also shows that context pollution is contagious and that simply ignoring assistant turns can worsen performance, underscoring the need for structured removal of assistant content.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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