Context Pruning for Coding Agents via Multi-Rubric Latent Reasoning
A new framework called LaMR has been proposed for improving coding agents by enhancing context pruning. This method decomposes code relevance into two dimensions: semantic evidence and dependency support, allowing for more effective filtering of irrelevant code. Experiments show that LaMR outperforms traditional methods in various benchmarks while saving token usage.
- ▪LaMR is a structured pruning framework that improves the relevance of code retrieved by coding agents.
- ▪It models code relevance through two dimensions, allowing for better retention of important code segments.
- ▪Experiments indicate that LaMR saves up to 31% more tokens and improves performance on multi-turn and single-turn tasks.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15315 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.15315 (cs) [Submitted on 14 May 2026] Title:Context Pruning for Coding Agents via Multi-Rubric Latent Reasoning Authors:Jingjing Wang, Xiwen Chen, Wenhui Zhu, Huayu Li, Zhengxiao He, Feiyang Cai, Ana S. Carreon-Rascon, Xuanzhao Dong, Feng Luo View a PDF of the paper titled Context Pruning for Coding Agents via Multi-Rubric Latent Reasoning, by Jingjing Wang and 8 other authors View PDF HTML (experimental) Abstract:LLM-powered coding agents spend the majority of their token budget reading repository files, yet much of the retrieved code is irrelevant to the task at hand.
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