Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models

ArXi:2510.04618v3 Announce Type: replace-cross Large language model (LLM) applications such as agents and domain-specific reasoning increasingly rely on context adaptation: modifying inputs with instructions, strategies, or evidence, rather than weight updates. Prior approaches improve usability but often suffer from brevity bias, which drops domain insights for concise summaries, and from context collapse, where iterative rewriting erodes details over time. We