Measuring Behavior Portability in Large Language Models
Large language models are increasingly deployed as autonomous decision makers, yet the behavioral mapping they exhibit can vary substantially across decision environments that are payoff-equivalent by construction-environments that share identical payoff-relevant structure but differ in surface presentation. This sensitivity renders suite-based evaluation fragile and raises a fundamental question of behavioral portability: how well does a behavioral mapping learned in one decision environment informative on another that preserves the same underlying incentive structure? We introduce a formal framework to measure this property. Our protocol fits an interpretable behavioral model on data po...
| Authors | Tianjia Dong, Nadav Kunievsky, James A. Evans |
|---|---|
| Primary category | cs.AI |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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