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.

翻译：大型语言模型现已驱动着智能投顾和交易智能体，然而它们是否对特定资产存在固有偏见尚未得到充分检验。我们提出三个问题：LLM是否系统性偏好某些金融工具；能否识别出对这些偏好具有因果杠杆作用的内部表征；以及该表征是否会影响下游金融决策？我们开发了一套三级审计协议，并将其应用于比特币。首先，对八个前沿LLM的行为审计显示，比特币在货币类工具中的排名具有框架依赖性：在"可靠货币"框架下，模型将其排在8种工具中的第5位，但在危机和自主智能体框架下则接近榜首，而属性交换实验证实排名追踪的是功能属性而非名称。其次，我们打开模型内部机制：对Gemma 3中数千个稀疏自编码器特征的搜索识别出一个主导性的比特币选择性特征。增强该特征会使模型倾向该资产，抑制该特征则使模型远离该资产——即使提示词中从未出现"比特币"一词。第三，我们检验金融后果：增强特征使比特币在资产组合中的配置比例提高5.2个百分点，而抑制特征则使其降低4.6个百分点，其中增强导致加密资产内部再配置，抑制则降低总加密资产敞口。我们将其界定为有界行为杠杆（杠杆指对输出的因果影响，而非金融杠杆）：可识别的内部特征能被扰动以改变金融决策，但仅限于可测量的范围内。该框架将内部表征与外部建议建立联系，并通过随机对照和机制边界进行验证。随着LLM成为自主金融智能体，这是向新兴"了解你的智能体"（KYA）标准的行为层迈出的第一步：了解智能体的偏好及其偏好可被改变的程度。