ACON: Optimizing Context Compression for Long-horizon LLM Agents 事件

ACON: Optimizing Context Compression for Long-horizon LLM Agents arXiv:2510.00615v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as agents in dynamic real-world environments, where success depends on maintaining precise records of actions and observations. However, the resulting unbounded context growth in long-horizon agentic tasks makes two critical bottlenecks: prohibitive inference memory costs and reasoning degradation due to irrelevant info