Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents

[2605.28850] Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2605.28850 (cs) [Submitted on 16 May 2026 ( v1 ), last revised 15 Aug 2026 (this version, v3)] Title: Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents Authors: Weicheng Xue View a PDF of the paper titled Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents, by Weicheng Xue View PDF HTML (experimental) Abstract: We study behavioral alignment and representation dynamics of large language model (LLM) agents in financial decision environments. TradeArena, an auditable trading-agent testbed with risk reports, execution simulation, memory, and replayable trajectories, lets us analyze how rationales, positions, and interventions evolve under market stress. Code and data artifacts are available through the \href{ this https URL }{TradeArena repository}. We find pre-failure signatures: planning embeddings drift from normal centroids, fused plan-risk representations separate normal from pre-drawdown states, and local manifolds exhibit effective-rank contraction. Across 80 rolling failure anchors and eight LLM trajectories, this pattern persists across hash, LSA, Transformer, and white-box hidden-state probes. Stress tests with CoT-free target weights, lexical controls, OHLCV noise, and false audits show that rationale-level contraction can vanish without rationales, while intent-space and fused signatures remain informative. Structured risk feedback can act as an external alignment signal without fine-tuning, but not as a universal performance enhancer: true audit feedback improves calibration for some models, returns for others, and exposes cases where placebo or hidden feedback has higher short-horizon return but weaker alignment diagnostics. A 51-stock intraday experiment reveals a correlation blind spot: LLM rationales justify exposure to coupled assets that the risk layer clips. Finally, a financial-audit task suite shifts comparison from ``which model trades best'' to whether models can audit trajectories, respect execution boundaries, reproduce artifacts, and avoid claim overreach. These results support a research claim, not a profitability claim: auditable risk feedback and representation trajectories reveal when LLM financial reasoning is aligning, drifting, or failing. Subjects: Machine Learning (cs.LG) ; Computational Finance (q-fin.CP) Cite as: arXiv:2605.28850 [cs.LG] (or arXiv:2605.28850v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2605.28850 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Weicheng Xue [ view email ] [v1] Sat, 16 May 2026 16:53:34 UTC (38 KB) [v2] Sat, 30 May 2026 16:08:37 UTC (40 KB) [v3] Sat, 15 Aug 2026 09:07:50 UTC (40 KB) Full-text links: Access Paper: View a PDF of the paper titled Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents, by Weicheng Xue View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-05 Change to browse by: cs q-fin q-fin.CP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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