arXiv:2605.24490v1 Announce Type: cross 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. We propose Market Regime Council (MRC), a cooperative multi-agent decision system that computes exact Shapley credits across all single, pairwise, and Grand-coalition outputs for online agent weighting. Instantiated with N=3 specialist agents, at each trading period, MRC recomputes coali
The proliferation of multi-agent LLM systems for complex decision-making, particularly in finance, necessitates robust credit assignment mechanisms to enhance performance and transparency.
This development addresses a fundamental challenge in multi-agent AI systems, offering a principled approach to agent weighting and transparency in critical applications like portfolio management.
Credit assignment in multi-agent LLM systems can now be more dynamic and fair, potentially leading to more adaptive and resilient AI decision-making.
- · AI developers
- · Quantitative hedge funds
- · Portfolio managers
- · LLM companies
- · Opaque AI decision systems
- · Static weighting models
Improved performance and reliability of multi-agent LLM systems in financial applications.
Broader adoption of multi-agent LLM systems across other complex decision-making domains beyond finance.
Increased regulatory scrutiny and demands for explainability in AI-driven financial products due to enhanced transparency capabilities.
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