「multimodal」の検索結果
21 件Science experiment videos demonstrate how experiments are performed, but offer no direct interaction for learn
Robot agents built on vision-language models depend on both the planner and its harness: the tools, feedback,
Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation
Multimodal large language models can recognize visual content yet struggle to reason reliably over it across m
Vision-language models can get individual judgments right while struggling to recover and consistently use the
Vision-Language-Action (VLA) models have demonstrated promising capabilities in robotic manipulation by integr
Across coding, web, and tool-use tasks, language-model agents are now routinely trained with the same recipe:
We present Youtu-Parsing-Omni, an omni-modal parser that converts documents, images, audio, and video into str
While frontier vision-language models demonstrate strong capabilities in user interface (UI) navigation, their
Human cognition flexibly draws on a shared perceptual foundation to understand environments, attend to referen
Recursive self-improvement requires models not only to acquire useful training experience, but also to decide
Humans routinely consume multimodal content, where textual and visual information is naturally interleaved. Al
Recent advances in multimodal large language models have increased interest in interaction paradigms for video
Diffusion vision-language models (DVLMs) process their entire input at every denoising step. Since visual toke
Perceiving human body motion is critical for multimodal systems that understand and assist people. Yet current
Evaluating human-AI co-design requires testing whether coding agents can translate real users' multimodal requ
Extending Vision-Language-Action models (VLAs) to humanoid robots requires coordinated whole-body control and
Locating relevant evidence in long inputs is a core capability of Multimodal Large Language Models. Prior stud
Robot foundation models are typically evaluated through costly closed-loop rollouts, motivating low-cost offli
Multimodal large language models (MLLMs) show promising reasoning capabilities, but reliable physical interact
We introduce SIMIT, a test-time self-improvement framework in which a vision-language model creates its own qu