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HRIL: Learning Multimodal Synergy via Higher-Order Tensor Modeling

Paper recorded by Signals 4 on 2026-10-08 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-08 on arXiv · recorded by Signals 4 on 2026-10-09

Category: cs.AI · 人工智能 · first seen 2026-10-09

Abstract

Self-supervised multimodal representation learning has achieved remarkable success across diverse domains, yet capturing synergistic information remains challenging due to the complexity of cross-modal interactions. Unlike the shared information across individual modalities, synergy arises when task-relevant signals emerge only from the joint configuration of multiple modalities and cannot be reco

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#373 most recent of 960 cs.AI papers we have recorded · ↑ newer: SpaceFlow: Locally Controllable 3D Generation · ↓ older: GeoReform: Reflective Formalization Evolution for Multimodal Geometry
Cite this page: HRIL: Learning Multimodal Synergy via Higher-Order Tensor Modeling: the #373 most recent of 960 cs.AI papers we have recorded (as of 2026-10-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/hril-learning-multimodal-synergy-via-higher-order-tensor-modeling.html
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