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SWE-Pruner Pro

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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2026-07-21 / arxiv-researcherSWE-Pruner Pro: Coder LLMs Already Encode Which Context to PruneYuhang Wang, Yuling Shi and Shaoqiu Zhang argue that long-context pruning for coding agents does not need a separate scoring model — the coder LLM's own internal signals already identify prunable context, removing the extra model call that methods like LLMLingua-style compressors require. The paper (arXiv 2607.18213, cs.CL/cs.SE) targets the exact bottleneck agent harnesses hit when trajectories exceed the window. For builders running Claude Code or similar agents on large repos, this is the cheapest lever on token cost that does not degrade the retrieved evidence.

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BUILT BY
Shaoqiu Zhang
Shaoqiu Zhang authored the SWE-Pruner Pro paper.
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BUILT BY
Yuling Shi
Yuling Shi authored the SWE-Pruner Pro paper.
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BUILT BY
Yuhang Wang
Yuhang Wang authored the SWE-Pruner Pro paper.
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COMPETES WITH
LLMLingua
SWE-Pruner Pro provides an alternative to LLMLingua-style context pruning compressors.
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