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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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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