Papers, modelos e datasets em alta no Hugging Face, além do blog oficial — com leitura editorial em português.
Dataset em destaque no Hugging Face — 35.4 mil downloads. Gaming Dataset (gaming-1) — 494.
Um modelo aberto de 3 bilhões de parâmetros transforma uma frase em coordenadas precisas na imagem — e o faz prevendo a caixa inteira de uma vez, com até 2,5× mais velocidade de decodificação.
Alimentado pelo Genie 3, o protótipo cria ambientes 3D navegáveis a partir de um prompt de texto. Funciona bem quando o pedido é uma fantasia em aquarela; tropeça quando tenta ser realista — e chega ao mercado no mesmo mês em que rivais levantaram mais de US$ 1 bilhão para brigar pela mesma categoria.
Modelo de geração de texto · MoE — 35.8 mil downloads e 587 curtidas no Hugging Face.
arXiv:2606.28546v1 Announce Type: new Abstract: Recent advances in AI-driven weather and climate modeling have improved forecast skill while reducing computational cost. However, existing data-driven approaches are limited in their ability to model coupled Earth system dynamics, which is required for extending predictability beyond the ~2-week horizon. To address this, we introduce NIVA, a multimodal foundation model designed to learn unified representations across Earth system components. While...
arXiv:2606.28406v1 Announce Type: new Abstract: Text-to-image and multimodal generative models are increasingly used to produce scientific figures such as mechanism diagrams, experimental-design schematics, conceptual frameworks, and graphical abstracts. Yet existing image-generation benchmarks (e.g., GenEval, T2I-CompBench, DPG-Bench) evaluate natural images and measure compositionality, object counting, or photorealism. None of them measure what makes a generated scientific figure usable: corr...
arXiv:2606.28938v1 Announce Type: new Abstract: Modern vision-language models (VLMs) for driving assistants typically treat vehicle dynamics as a black box, resulting in decisions that lack awareness of the vehicle's real-time electro-mechanical state. To bridge this gap, we introduce the Electro-Visual-Language Assistant (EVLA) -- a novel framework that combines multi-modal scene understanding with real-time perception of the electrified powertrain state (e.g., motor torque, battery SOC). Our a...
arXiv:2606.28520v1 Announce Type: new Abstract: Large vision-language models (LVLMs) are increasingly used for clinical image understanding, yet they remain vulnerable to \emph{hallucinations}--producing textual findings or attributes not supported by the image. We present a vision-traceable hallucination detection framework that audits arbitrary LVLM responses via visual evidence grounding, requiring neither modification nor internal access to the hidden states of LVLMs. Given an LVLM response,...
arXiv:2606.29028v1 Announce Type: new Abstract: Keypose-based manipulation decomposes tasks into critical waypoints to simplify policy learning for long-horizon tasks, but existing approaches rely on task-specific heuristics or manual annotation to extract keyposes from demonstrations. We present an automatic trajectory labelling pipeline for grasp-related tasks. This pipeline combines vision-language models (VLMs) for semantic event detection with classical trajectory analysis for precise tempo...
arXiv:2606.28397v1 Announce Type: new Abstract: Vision-language navigation (VLN) has recently advanced with large language and multimodal models, enabling agents to follow natural-language instructions in unseen environments without training a task-specific navigation policy. However, most existing VLN methods relying on large models still adopt an open-loop decision-execution approach, where candidate actions are generated from instructions and observations but are rarely verified or corrected ...
arXiv:2606.28760v1 Announce Type: new Abstract: Social robot navigation (SRN) requires more than geometric path planning; it demands understanding human intentions, social norms, and contextual cues to generate socially compliant behaviors. Although classical navigation methods provide reliable metric planning and collision avoidance, they often lack the semantic reasoning capabilities necessary for operation in complex human-centered environments. Recent advances in Vision-Language Models (VLMs...
arXiv:2606.28401v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown strong performance in visual understanding, yet they still suffer from hallucinations, generating content that is not grounded in the image. Preference alignment is a promising approach to improve visual faithfulness, but its success depends heavily on how preference pairs are constructed. Existing methods exhibit two key limitations; (a) intervention-based methods often introduce significant deviation from ...