Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents
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.
69d626e1c6ed9968ac5c6609
Ádám Kovács
claimed_verified
2026-04-08T16:34:07.174Z
646264832538819c729e32ba
https://cdn-avatars.huggingface.co/v1/production/uploads/646264832538819c729e32ba/syc-UpPQyR3Nbf-gYndc4.jpeg
Adam Kovacs
adaamko
user
adaamko
Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents
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.
A task-conditioned tool-output pruning model achieves high recall and F1 scores while dramatically reducing input token consumption compared to zero-shot and heuristic baselines.
tool-output pruning LoRA fine-tune zero-shot models heuristic pruning recall F1 score