Academic

Coding Agents are Effective Long-Context Processors

arXiv:2603.20432v1 Announce Type: new 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 thi

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Weili Cao, Xunjian Yin, Bhuwan Dhingra, Shuyan Zhou · · 1 min read · 13 views
#cs.CL #cs.AI

arXiv:2603.20432v1 Announce Type: new 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 familiarity, which allows them to navigate massive text corpora as directory structures. These findings suggest that delegating long-context processing to coding agents offers an effective alternative to semantic search or context window scaling, opening new directions for long-context processing in LLMs.

Executive Summary

This article presents a novel approach to improving the performance of Large Language Models (LLMs) in processing long contexts by delegating this task to coding agents. The authors evaluate off-the-shelf coding agents on various benchmarks, including long-context reasoning, retrieval-augmented generation, and open-domain question answering. The results show a significant improvement of 17.3% on average compared to state-of-the-art models. The authors attribute this success to two key factors: native tool proficiency and file system familiarity. This approach offers an effective alternative to traditional methods, such as semantic search or context window scaling, and opens new directions for long-context processing in LLMs. The findings have significant implications for the development of more efficient and effective LLMs.

Key Points

  • ▸ Coding agents outperform state-of-the-art LLMs in long-context processing tasks
  • ▸ Native tool proficiency and file system familiarity are key factors in the success of coding agents
  • ▸ This approach offers an effective alternative to traditional methods for long-context processing

Merits

Strength in Delegation

The authors successfully demonstrate the effectiveness of delegating long-context processing to coding agents, which can lead to significant improvements in model performance.

Methodological Innovation

The article presents a novel approach to long-context processing, which can inspire new directions in research and development of LLMs.

Demerits

Limited Generalizability

The results may not generalize to other domains or tasks, as the article focuses on a specific set of benchmarks and coding agents.

Dependence on Coding Agents

The approach requires the use of coding agents, which may not be feasible or practical in all scenarios.

Expert Commentary

This article presents a significant contribution to the field of natural language processing, particularly in the area of long-context processing. The authors' approach to delegating this task to coding agents offers a promising alternative to traditional methods. However, further research is needed to fully explore the potential of this approach and to address the limitations and challenges associated with its implementation. Additionally, the article's findings highlight the need for a more nuanced understanding of the role of attention mechanisms and semantic search in LLMs.

Recommendations

  • ✓ Further research is needed to explore the potential of coding agents in LLMs and to develop more effective and efficient models.
  • ✓ The development of more robust and scalable coding agents is essential to fully realize the benefits of this approach.

Sources

Original: arXiv - cs.CL

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