Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents
📰 ArXiv cs.AI
Squeez is a task-conditioned tool-output pruning method for coding agents to reduce unnecessary tool observations
Action Steps
- Identify the task and focused query for the coding agent
- Determine the relevant tool output for the task
- Apply task-conditioned tool-output pruning to extract the smallest verbatim evidence block
- Integrate the pruned output into the coding agent's workflow
Who Needs to Know This
AI engineers and researchers working on coding agents can benefit from Squeez to improve the efficiency of their models, while software engineers can utilize the method to optimize their development workflows
Key Insight
💡 Task-conditioned tool-output pruning can significantly improve the efficiency of coding agents
Share This
💡 Squeez reduces coding agent tool observations by up to 90%
Key Takeaways
Squeez is a task-conditioned tool-output pruning method for coding agents to reduce unnecessary tool observations
Full Article
Title: Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents
Abstract:
arXiv:2604.04979v1 Announce Type: cross Abstract: 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
Abstract:
arXiv:2604.04979v1 Announce Type: cross Abstract: 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
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