DarkForest: Less Talk, Higher Accuracy for Multi-Agent LLMs
The paper presents DarkForest, a new framework aimed at improving the accuracy of multi-agent large language models (LLMs) while reducing communication overhead. By allowing agents to operate independently and only sharing structured candidate records, DarkForest minimizes error propagation and enhances overall reasoning quality. Experimental results indicate that this approach can significantly outperform existing methods in terms of accuracy and efficiency.
- ▪DarkForest reduces communication overhead by keeping agents independent during the reasoning process.
- ▪The framework clusters semantically equivalent responses and estimates a calibrated belief distribution.
- ▪Experiments show that DarkForest improves baseline performance by up to 30.7% and reduces token consumption by up to 6.5 times.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.25188 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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Computer Science > Artificial Intelligence arXiv:2605.25188 (cs) [Submitted on 24 May 2026] Title:DarkForest: Less Talk, Higher Accuracy for Multi-Agent LLMs Authors:Yi Li, Songtao Wei, Dongming Jiang, Zhichun Guo, Qiannan Li, Bingzhe Li View a PDF of the paper titled DarkForest: Less Talk, Higher Accuracy for Multi-Agent LLMs, by Yi Li and Songtao Wei and Dongming Jiang and Zhichun Guo and Qiannan Li and Bingzhe Li View PDF HTML (experimental) Abstract:Multi-agent LLM systems improve reasoning by combining outputs from multiple agents, but interaction-heavy methods can introduce error propagation and high communication overhead.
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