AI RESEARCH
Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents
arXiv CS.LG
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ArXi:2605.28850v1 Announce Type: new We study behavioral alignment and representation dynamics of large language model (LLM) agents in financial decision environments. Using TradeArena, an auditable trading-agent testbed with risk reports, execution simulation, memory, and replayable trajectories, we analyze how rationales, positions, and interventions evolve under market stress.