Introduces failure-aware observability framework for diagnosing wasted computation in multi-agent LLM systems and evaluates it on 165 GAIA traces showing common operational failures.
Recursive Multi-Agent Trading System: Iterative Optimized Portfolio Strategy Under Geopolitical Uncertainty
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abstract
Recursive Multi-Agent Trading System (RMATS) integrates four specialized agents -- Sentiment, Report, Analysis, and Risk -- coordinated through a recursive Manager Agent with iterative feedback loops. Experimental evaluation over a 561-trading-day period (January 2023 to March 2025) across a 24-asset multi-class universe demonstrates that RMATS achieves a maximum drawdown of 9.62%, lower than MVO (15.49%) and FinBERT Sentiment (15.28%), and exhibits the lowest event-period drawdown in 3 of 5 geopolitical stress scenarios tested. While RMATS underperforms return-maximizing baselines in a sustained bull market environment, ablation studies confirm the individual contribution of each agent component to downside protection. These results position RMATS as a risk-control-oriented architecture suitable for institutions prioritizing capital preservation under geopolitical uncertainty.
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2026 1verdicts
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Early Diagnosis of Wasted Computation in Multi-Agent LLM Systems via Failure-Aware Observability
Introduces failure-aware observability framework for diagnosing wasted computation in multi-agent LLM systems and evaluates it on 165 GAIA traces showing common operational failures.