Reevaluating Self-Consistency Scaling in Multi-Agent Systems

📝 Original Info

• Title: Reevaluating Self-Consistency Scaling in Multi-Agent Systems
• ArXiv ID: 2511.00751
• Date: 2025-11-02
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (원문에 저자 명단이 포함되지 않음) **

📝 Abstract

This study examines the trade-offs of increasing sampled reasoning paths in self-consistency for modern large language models (LLMs). Earlier research with older models showed that combining multiple reasoning chains improves results before reaching a plateau. Using Gemini 2.5 models on HotpotQA and Math-500, we revisit those claims under current model conditions. Each configuration pooled outputs from varying sampled reasoning paths and compared them to a single chain-of-thought (CoT) baseline. Larger models exhibited a more stable and consistent improvement curve. The results confirm that performance gains taper off after moderate sampling, aligning with past findings. This plateau suggests diminishing returns driven by overlap among reasoning paths. Self-consistency remains useful, but high-sample configurations offer little benefit relative to their computational cost.

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