[2603.20432] Coding Agents are Effective Long-Context Processors

Computer Science > Computation and Language arXiv:2603.20432 (cs) [Submitted on 20 Mar 2026] Title:Coding Agents are Effective Long-Context Processors Authors:Weili Cao, Xunjian Yin, Bhuwan Dhingra, Shuyan Zhou View a PDF of the paper titled Coding Agents are Effective Long-Context Processors, by Weili Cao and 3 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) have demonstrated remarkable progress in scaling to access massive contexts. However, the access is via the latent and uninterpretable attention mechanisms, and LLMs fail to effective process long context, exhibiting significant performance degradation as context length increases. In this work, we study whether long-context processing can be externalized from latent attention into explicit, executable interactions, by allowing coding agents to organize text in file systems and manipulate it using its native tools. We evaluate off-the-shelf frontier coding agents as the general interface for tasks that require processing long contexts, including long-context reasoning, retrieval-augmented generation, and open-domain question answering with large-scale corpus contains up to three trillion tokens. Across multiple benchmarks, these agents outperform published state-of-the-art by 17.3% on average. We attribute this efficacy to two key factors: native tool proficiency, which enables agents to leverage executable code and terminal commands rather than passive semantic queries, and file system...