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arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).
arXiv:2607. 13881v1 Announce Type: cross Abstract: Human-object interaction detection (HOID) has traditionally been formulated as a supervised detection problem over predefined interaction categories.
arXiv:2608. 15869v1 Announce Type: cross Abstract: Multimodal large language models increasingly use visual chain-of-thought (Visual CoT) to reason about spatial, temporal, and embodied environments.
arXiv:2608. 03450v1 Announce Type: cross Abstract: Reasoning in Multimodal Large Language Models (MLLMs) requires both fine-grained visual perception and rigorous logical deduction.
arXiv:2605.27310v2 Announce Type: replace Abstract: Cross-view spatial reasoning remains a weak spot for vision-language models (VLMs): they reason in language and discard the fine-grained geometry t...
WorldCast is a benchmark that tests multimodal generative models on multi-step world state prediction, requiring them to forecast successive future states from multimodal observations and diverse references. The study evaluates 12 models across 320 manually designed cases in eight categories, revealing that advanced models like GPT-Image-2.5 achieve high scores while open-source models lag behind, especially on complex context and reasoning tasks. The results underscore the importance of integrating dynamic planning, reasoning, and unified multimodal capabilities for realistic world simulation.