Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents
Summary: arXiv:2604.04979v1 | Announce Type: cross
Introduction
In the realm of artificial intelligence, particularly in coding agents, the efficiency of processing tool outputs remains a significant challenge. Coding agents often encounter lengthy tool observations, yet only a fraction of these inputs is necessary for subsequent actions. The research titled “Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents” investigates the concept of task-conditioned tool-output pruning, aiming to streamline the information consumed by coding agents.
Research Overview
The core objective of this study is to enable coding agents to focus on the most relevant information for their tasks. By implementing a mechanism that identifies and returns the smallest relevant block of evidence from tool outputs, the research aims to enhance the efficiency of coding agents in processing information.
Benchmark Creation
The researchers established a comprehensive benchmark comprising 11,477 examples derived from interactions with the SWE-bench repository and synthetic outputs from multi-ecosystem tools. This extensive dataset was further refined with a manually curated test set containing 618 examples to ensure rigorous evaluation.
Methodology
To validate their approach, the researchers fine-tuned the Qwen 3.5 2B model using Low-Rank Adaptation (LoRA). The performance of this fine-tuned model was then compared against larger zero-shot models, specifically the Qwen 3.5 35B A3B, as well as various heuristic pruning baselines.
Results
The results of the study demonstrated significant advancements in recall and F1 scores. The fine-tuned model achieved:
- Recall: 0.86
- F1 Score: 0.80
- Input Token Reduction: 92%
These outcomes indicate that the proposed method not only enhances the model’s ability to recall relevant information but also leads to a substantial reduction in the number of input tokens processed, thereby improving overall efficiency.
Comparative Analysis
In comparison to the zero-shot Qwen 3.5 35B A3B, the fine-tuned model outperformed it by a notable margin of 11 recall points. Additionally, the performance surpassed all heuristic baselines by a wide margin, underscoring the effectiveness of the task-conditioned pruning approach.
Conclusion
The findings from the Squeez research highlight a promising direction for enhancing the capabilities of coding agents through task-conditioned tool-output pruning. By minimizing the amount of irrelevant information processed, coding agents can operate more efficiently, leading to improved performance in coding tasks. This research sets the stage for future advancements in AI-driven automation and coding assistance.
Future Work
Looking ahead, further exploration into more complex task conditions and the application of this pruning technique across various domains could yield even more substantial benefits. The implications of this research could pave the way for more sophisticated AI systems capable of handling intricate coding and automation tasks with greater agility.