# Multi-Agent Debate for Explainable Trading: Reasoning, Consensus, and Performance in Simulated Markets

arXiv: https://arxiv.org/abs/2609.29701 · HTML: https://mlsanity.com/paper/2609.29701/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 8 of 8)

- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [Prompting Large Language Models With the Socratic Method](https://mlsanity.com/paper/2303.08769/) (2023)
- [ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets](https://mlsanity.com/paper/2110.14771/) (2021)
- [ABIDES: Towards High-Fidelity Market Simulation for AI Research](https://mlsanity.com/paper/1904.12066/) (2019)
- [AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions](https://mlsanity.com/paper/2508.11152/) (2025)
- [RAudit: A Blind Auditing Protocol for Large Language Model Reasoning](https://mlsanity.com/paper/2601.23133/) (2026)
- [Internal Reasoning vs. External Control: A Thermodynamic Analysis of Sycophancy in Large Language Models](https://mlsanity.com/paper/2601.03263/) (2025)

## Abstract (arXiv)

Large language models (LLMs) are increasingly used for financial decision-making, yet it remains unclear whether improvements in reasoning quality translate into better economic outcomes. We investigate this question using a multi-agent debate framework for portfolio allocation in historical market simulations, where specialized agents propose, critique, and revise investment decisions. Reasoning quality is evaluated across four dimensions: logical validity, evidential support, alternative consideration, and causal alignment, and compared with downstream financial performance. Across 210 controlled runs, aggregate reasoning quality shows no meaningful relationship with Sharpe ratio (r = 0.07, p = 0.29) or total return (r = 0.03, p = 0.70). Structured prompting increases measured reasoning quality from about 0.72 to 0.84 (+17.7%, Cohen's d about 2.0), but these gains do not consistently translate into higher returns. We identify sycophantic convergence as a central failure mode, where agents abandon independent positions during critique-revision cycles and converge toward similar allocations. A Jensen-Shannon divergence intervention that preserves disagreement improves Sharpe by +0.14 (p = 0.028) and Sortino by +0.25 (p = 0.026), while interventions enforcing stronger causal reasoning do not improve financial performance. Our results suggest that multi-agent debate is most valuable when it preserves independent informational signals rather than simply improving measured reasoning quality.