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July 15, 2026ACM SIGOPS Operating Systems Review

DOTA: Intelligent Debugging with D elta o f T houghts A gents

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Authors

RYRui YangRGRajiv GuptaQZQian Zhang

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Overview

Randomized trial demonstrates improved bug detection in code, indicating better debugging capabilities.

Key Points

  • This research aims to improve the debugging capabilities of large language models through a collaborative multiagent framework.
  • Developed DoTA framework utilizing multiagent collaboration for code debugging.
  • Enhanced code understanding via automated hierarchical documentation analysis.
  • Evaluated on the Debug-Bench dataset with 4,253 debugging instances.
  • Achieved an average improvement of 15% in bug detection accuracy over GPT-3.5 (P<0.001).
  • Showed +18.3% improvement in handling complex logical errors.
  • Enhanced bug detection capabilities by 10.4–13.5% on open-source models.

Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/6a57231a88b21df87547ff6chttps://doi.org/10.1145/3830422.3830424
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Also Consider

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  1. 1A Unified Debugging Approach via LLM-Based Multi-Agent Synergy2024 · 6 citations
  2. 2LDB: A Large Language Model Debugger via Verifying Runtime Execution Step-by-step2024 · 4 citations
  3. 3NL-Debugging: Exploiting Natural Language as an Intermediate Representation for Code Debugging2025
  4. 4Debugging Engine Enhanced by Prior Knowledge: Can We Teach LLM How to Debug?2026 · 1 citations
  5. 5DePro: Understanding the Role of LLMs in Debugging Competitive Programming Code2026