Summary of Swe-search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement, by Antonis Antoniades et al.
SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement
by Antonis Antoniades, Albert Örwall, Kexun Zhang, Yuxi Xie, Anirudh Goyal, William Wang
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed SWE-Search framework integrates Monte Carlo Tree Search (MCTS) with a self-improvement mechanism, enhancing software agents’ performance on repository-level software tasks. The framework consists of three agents: SWE-Agent for adaptive exploration, Value Agent for iterative feedback, and Discriminator Agent facilitating multi-agent debate for collaborative decision-making. By leveraging Large Language Models (LLMs) for both numerical value estimation and qualitative evaluation, the framework enables self-feedback loops where agents refine their strategies based on quantitative and qualitative assessments of pursued trajectories. Compared to standard open-source agents without MCTS, SWE-Search demonstrates a 23% relative improvement in performance across five models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Software engineers need to adapt quickly to changing requirements and learn from experience. Current software agents can’t backtrack or explore alternative solutions when their initial approaches don’t work. The new SWE-Search framework helps by combining two ideas: Monte Carlo Tree Search (MCTS) and self-improvement. This lets agents refine their strategies based on both numerical and natural language assessments of what they’ve tried so far. When tested, the framework improved performance by 23% compared to standard agents. |
