Title: CEA: Context Engineering Agent for Enhanced Reliability and Sustainability in Deep Research Systems
Primary area: foundation or frontier models, including LLMs
Keywords: Deep Research, LLM, Agent, Context Engineering, AgentRL, long context

Abstract: The increasing capacity of frontier models to process long contexts has fueled enthousiasm for deep research agents. However, longer contexts alone do not guarantee better responses. In fact, context overloading can lead to unexpected agent failures. 
To tackle this challenge, we introduce an autonomous context control framework built around a Context Engineering Agent (CEA). The CEA maintains structured context by efficiently managing historical interactions, tracking ongoing progress, and identifying critical clues, hence achieving an optimal trade-off between token efficiency and memory integrity.
In conjunction with this framework, we introduce CERL, an end-to-end multi-turn reinforcement learning method designed for CEA. We enhance training by filtering out trajectories with non CEA-attributable errors before gradient updates, thereby enhancing the stability of training.
Our \textbf{CEA} approach has demonstrated substantial efficacy in enhancing performance on complex information-seeking tasks, as evidenced by increased interaction sustainability and notable performance improvements across various benchmarks.
Despite its sophisticated context-processing mechanisms, CEA is a plug-and-play solution that seamlessly integrates into existing systems, enhancing agents' context management with minimal code modifications. This combination of internal sophistication and external simplicity makes CEA both powerful and practical for real-world deployment.

TL;DR: We propose an autonomous context control framework to address the context rot issue in large language model agents for better response quality.