BehaviorBench (Behavioral Traces) - Belief Confidence Error (ProfileGen): leaderboard

BehaviorBench (Yang et al., 2026) tests whether a model can predict what a real pseudonymous user will do, using 2,000 wallets reconstructed from public prediction-market and on-chain records with chronologically held-out targets. The model sees a structured profile summarizing the past behavior of the wallet (ProfileGen). The board is the mean absolute error, on a 0 to 1 scale, of the predicted share of the final position of the user held on that side; lower is better.

Metric: Confidence mean absolute error on a 0 to 1 scale: gap between the predicted and revealed share of the final position held on the dominant side; history interface ProfileGen (a released structured profile summarizing the wallet's pre-target behavior); 2,000 pseudonymous prediction-market wallets reconstructed from public Polymarket metadata and Polygon on-chain logs, 22,204 chronologically held-out wallet-market test instances; context-only baseline 0.3417; lower is better. Source: arxiv.org. Saturation forecast: Estimated already saturated. 11 models tracked.

Top models

#ModelScoreOverall rank
1Claude 3.7 Sonnet0.08#458
2Claude Opus 40.08#322
3Gemma 3 12B (IT)0.08#1837
4Gemma 3 27B (IT)0.09#1289
5GPT-OSS-20B0.1#1147
6GPT-5.4 Mini0.13#389
7Llama 4 Scout Instruct0.13#1543
8GPT-5.4 Nano0.15#635
9GPT-5.40.18#123
10Ministral-3-14B-Instruct-25120.19#1593
11NVIDIA-Nemotron-3-Nano-30B-A3B-BF160.2#1767

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=behaviorbench-behavioral-traces-belief-confidence-error-profilegen · How It Works · Data refreshed daily, snapshot 2026-11-13.