金融AI开发者终于有了检测模型资产偏好的系统方法——这项研究揭示了LLM在投资建议中可能隐藏的偏见，做量化交易或智能投顾的团队值得关注，可以直接用其审计框架测试自己的模型。
该研究开发了一个三级审计协议，用于检测大型语言模型（LLMs）是否对特定金融资产存在系统性偏好。以比特币为测试对象，对8个前沿LLM的行为审计发现，比特币在“可靠货币”框架下排名中等，但在危机和自主代理框架下排名靠前。通过分析Gemma 3模型的内部表征，研究人员识别出一个主导比特币选择的稀疏自编码器特征，增强或抑制该特征会显著改变模型对比特币的偏好，即使提示中未出现“比特币”一词。该特征还能影响下游投资组合分配：增强特征使比特币配置比例提高5.2个百分点，抑制则降低4.6个百分点。研究将这种影响定义为“有界行为杠杆”，为未来“了解你的代理”（KYA）标准奠定了基础。
Auditing Asset-Specific Preferences in Financial Large Language Models: Evidence from Bitcoin Representations and Portfolio Allocation
Large language models now power robo-advisors and trading agents, yet whether they carry built-in biases toward specific assets is largely untested. We ask three questions: do LLMs systematically prefer certain financial instruments; can an internal representation with causal leverage over those preferences be identified; and does that representation affect downstream financial decisions? We develop a three-level audit protocol and apply it to Bitcoin. First, a behavioral audit of eight frontier LLMs shows that Bitcoin's ranking among money-like instruments is frame-dependent: models place it around rank 5 of 8 as "reliable money" but near the top under crisis and autonomous-agent frames, and an attribute-swap experiment confirms rankings track functional properties, not names. Second, we open a model's internals: a search across thousands of sparse-autoencoder features in Gemma 3 identifies a dominant Bitcoin-selective feature. Amplifying it shifts the model toward the asset and suppressing it shifts the model away, even when "Bitcoin" never appears in the prompt. Third, we test financial consequences: amplification raises Bitcoin's portfolio share by 5.2 percentage points while suppression lowers it by 4.6 pp, with amplification reallocating within crypto and suppression cutting total crypto exposure. We characterize this as bounded behavioral leverage (leverage meaning causal influence over outputs, not financial leverage): an identifiable internal feature can be perturbed to move financial choices, but only within measurable limits. The framework links internal representations to external recommendations, validated with random controls and mechanism boundaries. As LLMs become autonomous financial agents, this is a first step toward a behavioral layer for emerging know-your-agent (KYA) standards: knowing what an agent prefers, and how far that preference can be moved.