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

SWE-Pruner Pro uses a coding agent’s own internal representations to decide which lines of tool output to keep or prune, eliminating the need for a separate code classifier. A small prediction head produces line-level labels and uses a length-aware embedding keyed to each tool output’s line count. Across two open-weight backbones and four multi-turn benchmarks, the method saved up to 39% of prompt and completion tokens while preserving task quality. On MiMo-V2-Flash, it also improved SWE-Bench Verified resolution by 3.8% and Oolong long-context accuracy by 2.2 points, with bounded inference overhead.