Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915466523443200 |
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| author | Wang, Ji Chen, Kashing Song, Xinyuan Zhang, Ke Ai, Lynn Yang, Eric Shi, Bill |
| author_facet | Wang, Ji Chen, Kashing Song, Xinyuan Zhang, Ke Ai, Lynn Yang, Eric Shi, Bill |
| contents | Most existing Large Language Model (LLM)-based agent frameworks rely on centralized orchestration, incurring high deployment costs, rigid communication topologies, and limited adaptability. To address these challenges, we introduce Symphony, a decentralized multi-agent system which enables lightweight LLMs on consumer-grade GPUs to coordinate. Symphony introduces three key mechanisms: (1) a decentralized ledger that records capabilities, (2) a Beacon-selection protocol for dynamic task allocation, and (3) weighted result voting based on CoTs. This design forms a privacy-saving, scalable, and fault-tolerant orchestration with low overhead. Empirically, Symphony outperforms existing baselines on reasoning benchmarks, achieving substantial accuracy gains and demonstrating robustness across models of varying capacities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20019 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence Wang, Ji Chen, Kashing Song, Xinyuan Zhang, Ke Ai, Lynn Yang, Eric Shi, Bill Machine Learning Artificial Intelligence Computation and Language Multiagent Systems Most existing Large Language Model (LLM)-based agent frameworks rely on centralized orchestration, incurring high deployment costs, rigid communication topologies, and limited adaptability. To address these challenges, we introduce Symphony, a decentralized multi-agent system which enables lightweight LLMs on consumer-grade GPUs to coordinate. Symphony introduces three key mechanisms: (1) a decentralized ledger that records capabilities, (2) a Beacon-selection protocol for dynamic task allocation, and (3) weighted result voting based on CoTs. This design forms a privacy-saving, scalable, and fault-tolerant orchestration with low overhead. Empirically, Symphony outperforms existing baselines on reasoning benchmarks, achieving substantial accuracy gains and demonstrating robustness across models of varying capacities. |
| title | Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence |
| topic | Machine Learning Artificial Intelligence Computation and Language Multiagent Systems |
| url | https://arxiv.org/abs/2508.20019 |