The October 2025 papar provide an overview of Agent Context Optimization (ACON), a novel framework designed to enhance the efficiency and performance of Large Language Model (LLM) agents operating in complex, long-horizon tasks. ACON addresses the challenge of unbounded context growth—which increases costs and reduces effectiveness—by optimally compressing both environment observations and interaction histories into concise summaries. The framework uses a gradient-free guideline optimization pipeline where a capable LLM analyzes compression failures from contrastive trajectories to refine the compression instructions in natural language. Furthermore, the optimized compressor can be distilled into smaller models to reduce computational overhead, with empirical results demonstrating significant reductions in peak tokens and memory usage while preserving or even improving task accuracy across multiple benchmarks. Source: https://arxiv.org/pdf/2510.00615