Gemini 4 Argon — Model Card
Official model documentation covering evaluation, limitations and safety considerations. The date records the listed model-card update.
Language, images, speech and video. Explore cross-modal representations and generative systems without collapsing distinct capabilities into one ranking.
Official model documentation covering evaluation, limitations and safety considerations. The date records the listed model-card update.
Official documentation for an image-and-text model, including inputs, outputs, architecture dependencies and safety evaluation.
Human and robot demonstrations are combined with force-aware planning for dexterous manipulation.
Connects embodied understanding, interaction-centric world modeling and action learning for tabletop and whole-body humanoid manipulation.
A foreground-background architecture separates voice interaction from asynchronous task execution, coordinating authorization, state and delivery.
Official release introducing an asymmetric causal encoder-decoder architecture and linking its technical report and model resources.
A diffusion-model post-training pipeline combines task-specific rewards, reinforcement learning and on-policy distillation.
A language-conditioned video world model predicts future visual trajectories across manipulation, navigation and driving settings.
Describes a controllable multi-view world model used for driving simulation, scenario generation, and evaluation.
The report explores an omni-modal model family and training over heterogeneous visual, textual and audio data.
A dataset, VLA policy and capture setup connect human demonstrations to control across different dexterous hands.
Studies joint visual-language training and multi-agent task decomposition in an open multimodal model.
Continues training on robot manipulation data, connecting visual-language understanding and spatial instructions to action prediction.
A speech-recognition family and a forced-alignment model address multilingual transcription and timestamp prediction.
The Qwen vision-language family integrates text, image and video understanding across several model sizes.
Connects driving reasoning and action prediction through Cosmos-Reason and a diffusion trajectory decoder, studying reasoning-action consistency.
An industrial model-family report covering reasoning, multimodal input, long contexts and agent-related evaluations.
A coupled vision-language and diffusion architecture combines reasoning with continuous robot-action generation.
Gemini-based models address robot manipulation and embodied reasoning, connecting multimodal understanding with physical control.
A platform of video curation, tokenizers, pretrained world models and post-training for physical-AI applications.
Builds on Gemini to predict driving trajectories, perception objects and road structure from camera inputs and text.
Extends promptable segmentation from images to video using streaming memory and interactive data collection.
An open vision-language-action model and adaptation framework make generalist robot policy research more accessible.
Models video, text and vehicle actions as sequences to generate conditioned driving scenes and possible futures.
Vision-language models are trained to produce robot actions, exploring transfer from web-scale learning to robotic control.
The report describes GPT-4’s capabilities, evaluation results and limitations while leaving important training details undisclosed.
Robot control is formulated as conditional denoising of action sequences, linking diffusion models to visuomotor learning.
Contrastive training aligns images with natural-language descriptions, enabling transfer to new visual categories.
A generative model learns to reverse a gradual noise process, establishing an influential approach to image synthesis.
Documents a driving dataset and applications for sensor fusion, semantic understanding, and localization.
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
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