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arXiv AI By Yiming Yang, Zhuoyuan Li, Fanxiang Zeng, Hao Fu, Yue Liu

DIANOIA: Diagnostic Decomposition and Joint Optimization for Multi-Agent Reasoning

Read the original on arXiv AI →

DIANOIA introduces a diagnostic framework for multi‑agent large language model systems, decomposing reasoning gain into three measurable channels—coverage, fidelity, and synthesis. The protocol identifies bottleneck channels for a given task and implements a corresponding multi‑agent system with role‑diverse proposers, execution‑grounded verification, and iterative synthesis. Experiments on GSM8K, AIME‑2025, MBPP, and BFCL‑SP show that DIANOIA outperforms strong baselines, achieving significant token savings and accuracy gains while accurately pinpointing the critical channels.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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