Disentangled Skill Representations for Predictive Human Modeling

2026-08-24

AI Summary

本文提出SAIL方法，通过自然行为推断人类技能的多维结构，实现鲁棒且可解释的技能表示，以解决技能建模问题。

Abstract

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 behavior prediction that generalizes across a variety of in-domain contexts. We represent each individual with a persistent skill embedding that controls a blend between expert and novice bases and is trained using counterfactual subskill swaps for disentanglement. This design encourages representations that are both robust to performance variation and structured for interpretability. We demonstrate across racing and baseball that SAIL achieves strong predictive performance and consistently improves behaviorally grounded disentanglement over the evaluated baselines, while also improving downstream AI coaching performance.

Problem

• human skill
• latent variable
• behavioral grounding

Innovation

• Skill Abstraction
• Interpretable Latents
• Disentanglement
• Behavior Prediction
• Transferable Representation