✨DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems

📝 Summary:
DoVer is an intervention-driven debugging approach for LLM multi-agent systems. It validates failure hypotheses and measures progress via targeted interventions, improving reliability. DoVer converts 18-49% of failed tasks into successes, offering an outcome-oriented debugging method.

🔹 Publication Date: Published on Dec 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06749
• PDF: https://arxiv.org/pdf/2512.06749
• Project Page: https://aka.ms/DoVer

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✓ https://t.iss.one/DataScienceT

#LLM #MultiAgentSystems #Debugging #AI #Research

✨SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications

📝 Summary:
SWE-SQL introduces BIRD-CRITIC, a new benchmark for SQL issue debugging, and Six-Gym, a training environment using f-Plan Boosting. Their open-source Bird-Fixer agent surpasses proprietary LLMs like GPT-4.1 in performance, democratizing advanced SQL-debugging capabilities.

🔹 Publication Date: Published on Jun 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.18951
• PDF: https://arxiv.org/pdf/2506.18951
• Project Page: https://bird-critic.github.io
• Github: https://github.com/bird-bench/BIRD-CRITIC-1

✨ Datasets citing this paper:
• https://huggingface.co/datasets/birdsql/bird-critic-1.0-flash-exp
• https://huggingface.co/datasets/birdsql/bird-critic-1.0-open
• https://huggingface.co/datasets/birdsql/bird-critic-1.0-postgresql

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#SQL #LLM #AI #Debugging #OpenSource