Multimodal Graph-of-Thoughts (MM-GoT) is introduced, a training-free, verification-constrained inference framework that integrates cross-modal grounding directly into hypothesis graph search and consistently outperforms chain-of-thought reasoning baselines under matched inference budgets.
This work introduces Spatially-Grounded Thought Tokenization to explicitly align special tokens with spatial locations for clear spatial correspondence and visual interpretability, and proposes Mixture-of-Thought-Tokens, a new free-form multimodal grounding method that bridges the perception-reasoning gap.
Tianyi Gao, Han Fang, Tianyi Ding et al.· arXiv.org· 0 citations
This work proposes CaRGo-T (Causal Reasoning Graph-of-Thought), a reasoning framework that represents the causal and contextual relationships underlying multimodal humor as a lightweight graph-based reasoning structure.
Abhilash Nandy, Rahul Seetharaman, Aman Bansal et al.· 0 citations
GLaQ, a grounded latent-query framework that replaces sequential latent rollout with a fixed set of context-conditioned queries grounded in the original visual tokens, is introduced, suggesting that direct query-to-image grounding can recover localized evidence from the full image without external visual operations or...
Ze-Sheng Yang, Lingling Zhang, Xinyu Zhang et al.· 1 citation
This work investigates cross-modal information flow in a video-based generative multiple-choice-like setting by applying a layer-wise causal intervention on video-text attention pathways and identifies a distinct pattern in temporal reasoning.
Davide Testa, Hugh Mee Wong, Alessandro Lenci et al.· 0 citations
M$^3$R-Reasoner is proposed, which combines curriculum-based reasoning supervision with task-aware reinforcement learning to align model reasoning with metaphor interpretation and improves Visual Evidence and Sentiment Justification scores over GPT-5.
Hongye Jiang, Jun-Nan Zhu, Jing-Wang Huang et al.· 0 citations
Across six diverse visual reasoning tasks including multi-view spatial reasoning and collision prediction, ReImaGin consistently outperforms both text-only reasoning and specialist vision-tool baselines, with gains of up to 25\%, demonstrating the advantage of flexible, generative visual reasoning.
Nishad Singhi, H. P. Rodriguez, Aditya Arora et al.· 0 citations
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