Group Cognition Learning: Making Everything Better Through Governed Two-Stage Agents Collaboration
Centralized multimodal learning commonly compresses language, acoustic, and visual signals into a single fused representation for prediction. While effective, this paradigm suffers from two limitations: modality dominance, where optimization gravitates towards the path of least resistance, ignoring weaker but informative modalities, and spurious modality coupling, where models overfit to incidental cross-modal correlations. To address these, we propose Group Cognition Learning (GCL), a governed collaboration paradigm that applies a two-stage protocol after modality-specific encoding. In Stage 1 (Selective Interaction), a Routing Agent proposes directed interaction routes, and an Auditing ...
| Authors | Chunlei Meng, Pengbin Feng, Rong Fu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 1 May 2026 |
| Last updated at source | 11 May 2026 |
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