SOURCE-LINKED INTELLIGENCE
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
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-24T19:13:47.000Z
First collected: 2026-09-21T10:22:00.206Z. This is not the publication date.