Low-dimensional latent spaces identify the functional structure of individual behavioral phenotypes
Extracting stable individual traits from behavior observed across diverse contexts is a central challenge in behavioral modeling. We propose a framework for inferring domain-invariant individual latent representations by jointly encoding behaviors across multiple domains. Using large-scale telemetry data from professional Counter-Strike 2 gameplay, we demonstrate that these representations are stable across distinct environments and roles, improving behavior prediction in novel domains. Our analysis reveals that complex idiosyncratic movement policies can be effectively compressed into low-dimensional embeddings, with as few as two dimensions capturing the majority of individual strategic variation. Crucially, the learned latent space forms a structured metric space where Euclidean distances predict the degradation of transfer performance. Furthermore, we show that the latent axes align with interpretable behavioral phenotypes, such as risk-taking and social cohesion. These findings suggest that multi-domain integration is a robust method for uncovering the functional structure of latent individuality in complex decision-making tasks, bridging the gap between high-dimensional telemetry data and meaningful psychological constructs.