Market Regime Council for Dynamic Credit Assignment in Multi-Agent LLM Decision Systems
The article presents a new cooperative multi-agent decision system called Market Regime Council (MRC) for portfolio management. MRC addresses issues of credit assignment among agents and improves transparency in decision-making. The system has shown significant performance improvements in trading across various crypto assets.
- ▪MRC computes exact Shapley credits for online agent weighting across multiple outputs.
- ▪The system achieved a Sharpe ratio of 1.51 and a cumulative return of 440.1% over 1,037 trading days.
- ▪Ablation results indicate that performance gains are due to Shapley-weighted integration rather than individual stages.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24490 |
| 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:2605.24490 (cs) [Submitted on 23 May 2026] Title:Market Regime Council for Dynamic Credit Assignment in Multi-Agent LLM Decision Systems Authors:Yunhua Pei, Zerui Ge, Jin Zheng, John Cartlidge View a PDF of the paper titled Market Regime Council for Dynamic Credit Assignment in Multi-Agent LLM Decision Systems, by Yunhua Pei and 3 other authors View PDF HTML (experimental) Abstract:Multi-agent LLM decision systems for portfolio management still lack a principled way to assign credit across specialist agents, remain vulnerable to cold-start dominance under regime shifts, and offer limited transparency into how final allocations are formed.
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