Large language models (LLMs) are increasingly deployed as autonomous agents in financial trading. However, they often exhibit a hazardous behavioral bias that we term uniform trust, whereby retrieved information is implicitly assumed to be factual and heterogeneous sources are treated as equally informative. This assumption stands in sharp contrast to human decision-making, which relies on selective filtering, cross-validation, and experience-driven weighting of information sources. As a result, LLM-based trading systems are particularly vulnerable to multi-source noise and misinformation, amplifying factual hallucinations and leading to unstable risk-return performance. To bridge this behavioral gap, we introduce TrustTrade (Trust-Rectified Unified Selective Trader), a multi-agent selective consensus framework inspired by human epistemic heuristics. TrustTrade replaces uniform trust with cross-agent consistency by aggregating information from multiple independent LLM agents and dynamically weighting signals based on their semantic and numerical agreement. Consistent signals are prioritized, while divergent, weakly grounded, or temporally inconsistent inputs are selectively discounted. To further stabilize decision-making, TrustTrade incorporates deterministic temporal signals as reproducible anchors and a reflective memory mechanism that adapts risk preferences at test time without additional training. Together, these components suppress noise amplification and hallucination-driven volatility, yielding more stable and risk-aware trading behavior. Across controlled backtesting in high-noise market environments (2024 Q1 and 2026 Q1), the proposed TrustTrade calibrates LLM trading behavior from extreme risk-return regimes toward a human-aligned, mid-risk and mid-return profile.

翻译：大型语言模型越来越多地被部署为金融交易中的自主代理。然而，它们常常表现出一种我们称之为“统一信任”的有害行为偏差，即默认将检索到的信息视为事实，并将异质来源视为同等信息量。这种假设与人类决策形成鲜明对比，后者依赖于对信息来源的选择性过滤、交叉验证以及经验驱动的加权。因此，基于大语言模型的交易系统特别容易受到多源噪声和错误信息的影响，这加剧了事实幻觉并导致不稳定的风险-收益表现。为弥合这种行为差距，我们提出“信任交易”（信任修正的统一选择性交易者），一种受人类知识启发启发的多代理选择性共识框架。信任交易通过聚合来自多个独立大语言模型代理的信息，并根据其语义和数值一致性动态加权信号，用跨代理一致性取代统一信任。一致性强的信号被优先处理，而分歧严重、基础薄弱或时间上不一致的输入则被选择性折扣。为进一步稳定决策，信任交易将确定性时间信号作为可重复的锚点纳入，并结合了一种反射记忆机制，该机制可在测试时（无需额外训练）自适应调整风险偏好。这些组件共同抑制了噪声放大和幻觉驱动的波动性，从而产生更稳定、更具风险意识的交易行为。在高噪声市场环境（2024年第一季度和2026年第一季度）的控制回测中，所提出的信任交易将大语言模型的交易行为从极端的风险-收益区间校准至与人类对齐的中风险、中收益特征。