Kaggle 联合 RunRehearsals 发布 Human Behavior Bench 人类行为预测基准
Kaggle 出了个测 AI 预测人类行为的新基准，1,244 个真人做底，Engram 1.0 比 Gemini 4 还准，榜单挺有意思。
Kaggle 与 RunRehearsals 推出 Human Behavior Bench，测试模型能否预测真实人群在健康、娱乐、消费等场景的决策。基准基于 1,244 名美国成年人的调查数据，共 750 个案例（149 公开、601 私有），用 1-TVD 打分。Rehearsals 的 Engram 1.0 以 94.1% 居首，超过人类噪声上限约 93；Gemini 4 Argon 得 80.6%，Claude Opus 5 得 80.2%，GPT-5.6 Sol 得 79.5%。LLM 的预测普遍向均值收敛，难以捕捉人群偏好的真实分布差异。
Can frontier models actually predict how real people behave? 🕺
Today, in collaboration with @RunRehearsals, we are launching Human Behavior Bench, the first public benchmark that tests whether AI can understand human preference and everyday decision-making.
AI models have gotten increasingly good at coding, but knowing for whom to build is the next frontier of challenges for AI models to capture. Human Behavior Bench evaluates models on predicting real human behavior across health, entertainment, recreation, values, and consumption.
To build this baseline, we gathered ground truth data directly from real people and evaluated models across a wide range of everyday scenarios:
• Ground truth baseline: 1,244 US adults surveyed (June-Sept 2026) • Some cohorts are traditional demographic indicators such as age, income, and location • Other filters are behavioral e.g. GLP-1 patients and recent car buyers • 750 cases total (149 public / 601 private) across finance, health, recreation, values, and consumption • Scored using 1-TVD (total variation distance)
Base models still have a significant gap to close to accurately predict human behavior by cohort. LLMs’ predictions tend to converge towards the mean, while the true distribution of human preferences vary greatly.
Against a human noise ceiling of ~93, the current leaderboard scores are: Rehearsals’ Engram 1.0 (AI Twins) tops the leaderboard at 94.1%, followed by Gemini 4 Argon at 80.6% Claude Opus 5 at 80.2%, GPT-5.6 Sol at 79.5%.
