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The Social World Model Evaluation Gap

Denglin Jiang (New York University) Annie Wang (New York University)
Oct 2026
Artificial Intelligence Natural Language Processing

Abstract

A negotiation assistant can reach a good deal without accurately predicting the other person's response. We ask whether evaluations of social world models test that prediction directly. We audit 113 works published from 1999 to 2026. 9 primary evaluations measure a real person's response to an action selected by the system or experimenter. Only 2 also compare an explicit response forecast with that response. Their predictors use information available when the action is chosen, and the evaluated responses were excluded from fitting; both randomize the intervention. A separate full-text check of 25 language-model works finds no experiment meeting these conditions. The other evaluations test current-state inference, task success, or prediction from recorded actions. We propose a 1-step negotiation experiment that separates these questions. The findings describe the audited sample, not the prevalence of this evaluation gap across the field.

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