Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents

Adam Kovacs

Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents: 6 upvotes on Hugging Face Daily Papers, #21 of 30 papers on 2026-04-08. Day-by-day upvote history. It lost 5 votes when the Hub removed votes in bulk.

Coding agents repeatedly consume long tool observations even though only a small fraction of each observation matters for the next step. We study task-conditioned tool-output pruning: given a focused query and one tool output, return the smallest verbatim evidence block the agent should inspect next. We introduce a benchmark of 11,477 examples built from SWE-bench repository interactions and synthetic multi-ecosystem tool outputs, with a manually curated 618-example test set. We fine-tune Qwen 3.5 2B with LoRA and compare it against larger zero-shot models and heuristic pruning baselines. Our model reaches 0.86 recall and 0.80 F1 while removing 92% of input tokens, outperforming zero-shot Qwen 3.5 35B A3B by 11 recall points and all heuristic baselines by a wide margin.

Paper page on Hugging Face · arXiv

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