Structure over Depth: A Single-Block Spatio-Temporal Transformer for Multi-Entity Reasoning
Modeling multi-entity temporal data requires capturing dependencies across entities, time, and their interactions. Transformer-based approaches perform well but often rely on deep stacks of layers to learn these heterogeneous dependencies implicitly, increasing computational cost. We revisit this problem from a structural perspective and decompose multi-entity temporal dynamics into three interaction types: spatial interactions among entities, temporal interactions across time, and cross interactions coupling the two domains. We propose a structured spatio-temporal transformer block that explicitly models all three within a single stage. It uses parallel spatial and temporal self-attentio...
| Authors | Narthana Sivalingam, Santhirarajah Sivasthigan, Buddhi Wijenayake et al. |
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| Primary category | cs.AI |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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