Policy Convergence and Explainable Stability in Orchestrated Multi-Agent LLM Trading Frameworks

This article examines orchestrated multi-agent large language model trading frameworks through the paired lenses of policy convergence and explainable stability. The topic has gained urgency because recent financial agent systems increasingly rely on distributed deliberation, memory, critique, and risk filtering, while evaluation practice still privileges return metrics over decision coherence and rationale persistence. The aim is to formulate an analytical framework for assessing whether specialized agents move toward a stable executable policy and whether the accompanying explanation remains consistent under workflow pressure, market variability, and iterative revision. The study draws on recent work in explainable AI for finance, explainable reinforcement learning, multi-agent reinforcement learning, quantitative trading, and LLM-based financial agents. Comparative source analysis, conceptual structuring, analytical synthesis, and cross-framework interpretation were applied. The analytical section identifies four determinants of stability: evidence discipline, deliberative topology, risk-gating logic, and adaptive memory control. The resulting framework supports research design, model auditing, and architecture selection for high-stakes AI trading systems.