Agents can spend a lot of context on raw pytest, grep, git log, kubectl, pip install, file reads, stack traces, etc., even though usually only a small block is relevant.
We've built benchmark for task-conditioned tool-output pruning and fine-tuned Qwen 3.5 2B on it with Unsloth. The benchmark is a combination of tool outputs from the SWE-bench dataset and synthetic examples.
Results on the held-out set:
- 86% recall
- 92% compression
- Beats other pruners and zero shot models (+11 recall over zero-shot Qwen 3.5 35B A3B)
We released squeez as a CLI, you can put it in front of tool output before the next reasoning step, or add it to something like CLAUDE md as a lightweight preprocessing step. You can serve squeez with any inference framework, e.g. VLLM.
Everything is open source, check out for details:
- paper: https://arxiv.org/abs/2604.04979
- model: https://huggingface.co/KRLabsOrg/squeez-2b
- dataset: https://huggingface.co/datasets/KRLabsOrg/tool-output-extraction-swebench
code: https://github.com/KRLabsOrg/squeez
If you are interested I can also post some examples / eval outputs.
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