从预测市场行为中提取人类认知差异，并尝试注入大模型以打破思维同质化。
Nous: An Attempt to Extract and Inject the Cognition Behind Prediction-Market Behavior
- 从真实交易数据中提取8维行为画像，识别出稳定认知特征。
- 部分认知特征与未来收益相关，但提示注入无法传递这些差异。
- 提示层改造无效，需深入模型内部（如微调）才能实现认知多样性注入。
随着大语言模型代理在预测市场和集体决策中普及，它们可能陷入认知同质化：基于相同基础模型的代理产生相关预测，近期测量显示前沿模型误差相关系数达 r ~ 0.77。本文探讨是否可从行为中恢复并转移人类认知多样性。Nous 从真实 Polymarket 交易活动提取结构化的八维行为画像，并通过提示注入到代理中。核心发现是该流程前后两阶段存在解耦：提取有效，100个钱包中14个参数有8个时间上稳定（分半信度 ICC ≥ 0.5，Bootstrap置信区间下界 > 0.3；逆向操作得分 ICC ~ 0.9）；钱包可通过画像识别，准确率远超随机（17-22% 对比 1%）；两个预设维度与未来实际收益呈秩相关，但经行为混杂控制后不显著。提示注入无效：在语义嵌入指标上，结构化注入未优于长度匹配对照组；所诱导的多样性既未降低集成误差相关性，也未提升 Brier 分数——这一零结果在采样温度、画像多样性、问题难度等探索性检验中均成立。对提示本身的测量表明，压缩发生在模型前：结构到叙事的转换器生成近似均匀的提示，其分布不反映画像差异。Nous 定位为测量认知同质化问题及提示层补救的局限性，推动更深层的模型内注入（如微调、激活操控）。代码、冻结画像、提示与模型输出见：https://github.com/WillChienT/nous-paper
原文摘要 · Abstract (English)
As LLM agents proliferate in prediction markets and collective decision-making, they risk a cognitive monoculture: agents built on shared foundation models produce correlated forecasts, and recent measurement finds frontier-model errors correlated at r ~ 0.77. We ask whether human cognitive diversity can be recovered from behavior and transferred to LLM agents. Nous extracts a structured eight-dimension behavioral profile from real Polymarket trading activity and injects it into agents through prompts. Our central finding is a dissociation between the two halves of that pipeline. Extraction works, partially: across 100 wallets, 8 of 14 parameters are temporally stable (split-half ICC >= 0.5, bootstrap CI lower bound > 0.3; contrarian score reaches ICC ~ 0.9); wallets are identifiable from their profiles well above chance (top-1 retrieval 17-22% vs. 1% chance); and two of four pre-specified dimensions rank-correlate with future realized profit out-of-sample, though the correlations do not survive behavioral-confound controls. Prompt-level injection does not measurably transmit it: on a semantic embedding metric, structured injection shows no significant advantage over a length-matched control on any model, and the diversity it induces neither reduces ensemble error correlation nor improves Brier score -- a null that persists across exploratory checks on sampling temperature, profile diversity, and question difficulty. Measuring the prompts themselves locates the compression before the model: the structure-to-narrative translator emits near-uniform prompts whose spread does not track profile spread. We position Nous as measuring the cognitive-monoculture problem and the limits of a prompt-level remedy, motivating deeper, below-the-prompt injection (fine-tuning, activation steering). Code, frozen profiles, prompts, and model outputs: https://github.com/WillChienT/nous-paper
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