SWE-Pruner Pro: The Coder LLM Already Knows What to Prune

Abstract:Pruning long context for coding agents has been a vital technology for efficient context management. While existing context pruning methods such as SWE-Pruner realize this by attaching a separate code classifier, we find the agent itself encodes internal representations indicating the relevance of code context when reading tool output. Based on this finding, we propose SWE-Pruner Pro, which prunes tool outputs directly inside the agent. Concretely, a small head turns the agent's own internal representations into a keep-or-prune label for each line, with a length-aware embedding keyed to each tool output's line count. Across two open-weight backbones and four multi-turn benchmarks, SWE-Pruner Pro saves up to 39% of prompt and completion tokens while preserving task quality, with bounded inference overhead. Notably, on MiMo-V2-Flash SWE-Pruner Pro additionally raises the SWE-Bench Verified resolve rate by +3.8% and the long-context Oolong accuracy by +2.2 points.

Computation and Language (cs.CL); Software Engineering (cs.SE)

arXiv:2607.18213 [cs.CL]

(or arXiv:2607.18213v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.18213 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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