SWE-Pruner Pro Shows Coding Agents Already Know What Context to Drop
rohanpaul_ai · x · 2026-07-22
The paper argues that a coding agent can use its own internal representations to decide which tool-output lines are irrelevant, instead of relying on a separate pruning model.
What SWE-Pruner Pro does
- Reads the agent’s last-layer hidden states and internal summaries.
- Predicts whether each tool-output line should be kept or pruned.
- Adds a length-aware signal so short outputs are not over-pruned.
- Uses a balanced loss to protect rare but important lines.
Results
- Trained only the added classification head on 22,609 labeled tool responses.
- Tested on 2 open models and 4 multi-turn benchmarks.
- Cut prompt/completion tokens by up to 39% while mostly preserving quality.
- On MiMo-V2-Flash, it improved one coding benchmark by 3.8% and one long-context test by 2.2 points.
The takeaway is that coding agents already encode enough relevance information to prune context without a separate model.
Related event: ByteDance's SWE-Pruner Pro Enables Coding Agents to Self-Prune Context(3 posts)→
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