Disentangled Skill Representations for Predictive Human Modeling
Understanding human skill is important for AI systems that collaborate with, coach, or assist people. Unlike typical latent variable estimation problems which rely on single observations, skill is a persistent, compositional, and behaviorally grounded construct that must be inferred from patterns over time. We introduce Skill Abstraction with Interpretable Latents (SAIL), a method for modeling human skill as an interpretable, multi-dimensional construct inferred from naturalistic behavior. Our approach produces a skill embedding that is robust to transient performance fluctuations and learns a transferable representation of human subskills. Furthermore, SAIL supports skill-informed behavi...
| Authors | Mariah Schrum, Deepak Gopinath, Srijan Srivatsa et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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