ACON: Optimizing Context Compression for Long-horizon LLM Agents

Understand

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 information.
• Existing compression methods fail to fully address this, often relying on brittle heuristics or requiring parameter updates impractical for proprietary or large-scale LLMs.
• We introduce Agent Context Optimization (ACON), a unified framework that optimally compresses both observations and history into concise, informative representations.