Title: Reading the Room: Learning Group States Beyond Pooled Individual Signals
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Multimodal Representation Learning, Group Dynamics, Emergent States, Social Signal Processing

Abstract: The fundamental challenge in modeling group dynamics is that collective states arise from interdependent processes that violate the standard assumption of independent observations. We formalize this as a learning problem where five constructs (group synchrony, group confidence, interaction phase, individual engagement, leadership) are not reliably recovered from pooled individual features due to the aggregation fallacy. We introduce SyntalNet, which addresses this through three architectural components: mask-aware encoders, permutation-equivariant cross-participant context gating (SoSE-X), and low-rank cross-modal fusion (GLRX). We construct Syntality, a benchmark derived from triadic interactions with temporally-smoothed crowd annotations for five constructs, formulated as directional trend prediction (increase/stable/decrease). Under 10-fold Leave-One-Group-Out cross-validation, SyntalNet outperforms the strongest baseline (Temporal CNN) across all five constructs (synchrony, confidence, interaction phase, engagement, leadership) and all metrics (balanced accuracy, F1, AUPRC, AUROC). For example, it achieves 0.64 balanced accuracy, 0.85 AUROC, and 0.63 F1 on engagement, and 0.60 balanced accuracy, 0.78 AUROC, and 0.58 F1 on leadership. Critically, we demonstrate that pooled individual signals yield performance statistically equivalent to constant predictors on group-level targets, highlighting the necessity of explicit cross-participant modeling. We provide an anonymized repo with code, processed features, and reconstruction instructions.