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