TBNAI
Joint, Decoupled, or Agent: When Each Multimodal Alignment Paradigm Wins
Publication: cs.AI updates on arXiv.org
Author: Yi Fang, Gaurav Srivastava, Sirui Ding, Xuan Wang
Source published: 20/10/2026, 04:00:00 UTC
Briefing created: 20/10/2026, 04:07:34 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 20/10/2026, 04:03:25 UTC.
Briefings
A 2026 study compared three multimodal alignment paradigms—Joint, Decoupled, and Agent—across 16 datasets and 144 training-label budgets. Results show Decoupled wins 54 cases, Joint 44, and Agent 46. On ScienceQA and MELD, preference shifted to fusion as supervision increased. The system selects the best paradigm using diagnostics and metadata, outperforming metadata-only routing.
Key points
- Decoupled training on frozen, independently pretrained encoders wins 54 out of 144 dataset-budget combinations.
- Supervised unimodal 5-NN accuracy predicts a shift toward Decoupled paradigms across eight classification datasets.
Context
This research investigates multimodal alignment strategies by comparing Joint, Decoupled, and Agent paradigms on 16 paired datasets across five domains. The findings clarify when each approach optimally performs, linking representation diagnostics to paradigm selection.
Limitations
Results are limited to 16 paired datasets and 144 dataset-budget combinations. No additional background context is supplied beyond the abstract.
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