2345 articles tagged with Multimodal
Modality Self-Play is a training framework for multimodal foundation models based on proxy-mediated modality composition.Source: arXivhttps://arxiv.org/abs/2610.20965#MachineLearni...
🎯 #LiquidAI released d1-ultra, a multimodal decision model for text and images, built for hard real-world cases like spotting a circuit board defect against its spec or checking a ...
Safety in Multimodal Large Language Models (MLLMs) depends on unsafe multimodal data for post-training alignment or activation steering. However, such data is hard to collect, must...
Vision-language-action (VLA) models have shown strong promise for generalist robotic manipulation, but their inference cost remains a major bottleneck for real-time control. This l...
Technology can help make the digital world more accessible. #AI powered tools such as Seeing AI use computer vision to help people who are blind or have low vision understand the w...
📰 openKylin Outlines Its Vision for Reimagining Linux in the AI Era at Open Source Summit Europe 2026At Open Source Summit Europe 2026, openKylin outlined its vision for transition...
Marvel's VisionQuest premiere sparked discussion over White Vision's identity, Ultron's return and J.A.R.V.I.S.'s sacrifice. Following WandaVision and Agatha All Along, the new Pau...
Native multimodal pre-training trains a single model from scratch on text and multimodal data. Replacing image embeddings with padding token embeddings affects visual-content ablat...
DecisionRobust benchmarks robustness for JEV style decision models across text and vision.Source: arXiv cs.CLhttps://arxiv.org/abs/2610.19118#MachineLearning
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle ...
Interactive agents repeatedly choose among a finite set of actions, labels, or ordinal outcomes. Generating a free-form response for each choice adds latency and complicates the in...
Reinforcement learning (RL) can elicit specialized visual capabilities in vision-language models (VLMs), yet jointly optimizing heterogeneous objectives often introduces cross-capa...
Vision-language-action (VLA) models transfer semantic knowledge acquired through vision-language pretraining to robot control, achieving strong performance across various benchmark...
Multimodal deep search is moving from short queries to long-horizon investigations that continually acquire evidence. However, training such agents remains constrained by limited c...
As Vision-Language Models (VLMs) advance, they are increasingly expected to perform spatiotemporal reasoning over motion, actions, and view changes beyond what the input images sho...
Retrieval is a fundamental task in multimodal systems: given an image or text query, the goal is to find the most relevant images or texts in a collection, often using a shared emb...
Unified multimodal models (UMMs) receive image-generation supervision that exposes them to visual information not fully documented in text. Can learning to generate images expand w...
Creative intent in video editing can be expressed through text instructions (e.g., "remove the people"), image references (e.g., subject appearance), video references (e.g., camera...
ActTune: Action-Aware Precision and GPU Operating-Point Adaptation for Energy-Efficient Vision-Language-Action Inference#LLM #Roboticshttps://hgpu.org/?p=31388
NEURA Robotics unveiled Neuron AI, a robot foundation model that processes touch, sound, and memory alongside vision. The model runs locally on five robot types. No benchmarks publ...
CEO Mark Lacerte Shares How the Wound Performance Platform Is Transforming Wound Care Through AI, Connected Clinical Workflows and Real-Time Analytics
MDN-VJEPA is a multimodal probabilistic JEPA that instantiates the latent predictor as a mixture density network representing future states with a Gaussian mixture. Controlled expe...
Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities...