From pose to behavior: SABER integrates identity-resolved multi-animal pose tracking with language-model-based behavioral factor discovery

Quantifying social behavior requires accurate assignment of posture and actions to individual animals, which is often hindered by close contact, occlusion, and identity switches. Meanwhile, current behavioral recognition pipelines still lack stable predictive accuracy. Here we developed SABER, a locally deployable framework that couples multi-animal pose estimation with identity-preserving tracking, interpretable behavioral factor mining, and multiscale temporal behavior prediction from a single overhead video stream. Across spontaneous social interaction, mating, aggression, and four-mouse recordings, SABER improved pose-estimation accuracy and tracking continuity relative to comparator pipelines. Its behavioral factor-mining procedure identified interpretable kinematic, postural, and social descriptors, and temporal integration improved classification of behavioral categories. SABER offers an intuitive, open-source interface to facilitate use. Applied to social-defeat-stress mice, SABER detected reduced approach behavior and a multivariate behavioral profile that distinguished depression-susceptible from control animals. SABER outputs could also be synchronized with miniscope calcium recordings, enabling joint analysis of behavioral states and neuronal population activity. SABER therefore provides an accessible, identity-resolved route from single-view social-interaction video to behavioral phenotyping and brain–behavior analysis.