Notable data and AI papers

627 papers · 45 labs

Robot Learning from a Physical World Model research paper by Google DeepMind, 2025

Google DeepMind · Multimodal and robotics · Nov 10, 2025 · 23 citations · 32 upvotes · unverified

Read on arXiv

What it shows

PhysWorld integrates video generation and physical world modeling to enable accurate robotic manipulation from visual demonstrations without real robot data.

By Jiageng Mao, Sicheng He, Hao-Ning Wu and 9 more · arXiv 2511.07416 · PDF · Code

UnverifiedHugging Face's summary; not yet checked by hand.

More from Google DeepMind

All 12
Foundation modelsUnverifiedJul 31, 2026DiffusionGemma Technical ReportDiffusionGemma is a fine-tuned mixture-of-experts language model that uses discrete diffusion to generate text blocks in parallel, achieving high speed while preserving capabilities like multimodal inputs and reasoning.Google DeepMind14 citations40 upvotesRead →Foundation modelsJul 2, 2026Gemma 4 Technical ReportOpen multimodal models from 2.3B to 31B parameters, with a thinking mode and image and audio input.Google DeepMind356 citations81 upvotesRead →Inference and efficiencyUnverifiedMay 17, 2026LiteFrame: Efficient Vision Encoders Unlock Frame Scaling in Video LLMsLiteFrame, a lightweight video encoder with Compressed Token Distillation training method, reduces latency and increases frame processing capacity for long-form video understanding in Video LLMs while maintaining accuracy.Google DeepMind2 citations26 upvotesRead →Foundation modelsUnverifiedMar 25, 2026Understanding the Challenges in Iterative Generative Optimization with LLMsGenerative optimization using large language models faces challenges due to implicit design decisions about artifact modification and learning evidence that significantly impact success across different applications.Google DeepMind11 citations27 upvotesRead →ReasoningUnverifiedMar 3, 2026LoGeR: Long-Context Geometric Reconstruction with Hybrid MemoryLoGeR enables long-term 3D video reconstruction by combining bidirectional priors with a hybrid memory system that includes parametric Test-Time Training and non-parametric sliding window attention mechanisms.Google DeepMind46 citations63 upvotesRead →Agents and evaluationUnverifiedDec 4, 2025SIMA 2: A Generalist Embodied Agent for Virtual WorldsSIMA 2, built on a Gemini foundation model, interacts in 3D virtual worlds, reasons about goals, handles complex instructions, and autonomously learns new skills through open-ended self-improvement.Google DeepMind27 citations26 upvotesRead →Agents and evaluationUnverifiedOct 8, 2025Vibe Checker: Aligning Code Evaluation with Human PreferenceVibe Checker evaluates LLMs by combining functional correctness and instruction following to better align with human coding preferences.Google DeepMind2 citations34 upvotesRead →Multimodal and roboticsUnverifiedSep 24, 2025Video models are zero-shot learners and reasonersVeo 3, a generative video model, exhibits zero-shot capabilities across various visual tasks, suggesting a trajectory towards becoming a unified, generalist vision foundation model.Google DeepMind237 citations100 upvotesRead →
Topic
Multimodal and roboticsApr 5, 2023Segment AnythingA promptable model and a dataset of over a billion masks that cut out any object in any image.Meta16.8k citations6 upvotesRead →Multimodal and roboticsJan 30, 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language ModelsBLIP-2 efficiently pre-trains vision-language models using off-the-shelf frozen encoders and decoders, achieving state-of-the-art performance with fewer parameters.Salesforce9,805 citations3 upvotesRead →Multimodal and roboticsJan 28, 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and GenerationBLIP, a Vision-Language Pre-training framework, improves performance across both understanding and generation tasks by bootstrapping captions from noisy web data, achieving state-of-the-art results on image-text retrieval, image captioning, and VQA, and showing strong generalization to video-language tasks.Salesforce7,655 citations5 upvotesRead →Multimodal and roboticsUnverifiedOct 25, 2024GPT-4o System CardGPT-4o is an omnimodal autoregressive model trained to handle text, audio, image, and video inputs, offering high-performance outputs across these modalities, with particular strengths in vision and audio.OpenAI5,388 citations88 upvotesRead →Multimodal and roboticsUnverifiedNov 20, 2025SAM 3: Segment Anything with ConceptsSegment Anything Model 3 achieves state-of-the-art performance in promptable concept segmentation and tracking by leveraging a unified model architecture with decoupled recognition and localization.Meta1,466 citations138 upvotesRead →Multimodal and roboticsUnverifiedMar 8, 2024DeepSeek-VL: Towards Real-World Vision-Language UnderstandingDeepSeek-VL is an open-source vision-language model that achieves state-of-the-art performance in real-world applications by combining a hybrid vision encoder with effective pretraining strategies to preserve language model capabilities.DeepSeek939 citations49 upvotesRead →
About this paper
Venue
arXiv.org
Citations
23, 2 influential · Semantic Scholar
Upvotes
32 · Hugging Face
Lab
Google DeepMind · on Companies · on Acquisitions

Changes

CitationsNov 9, 2026Citations rose from 22 to 23.
CitationsOct 12, 2026Influential citations rose from 1 to 2.
CitationsOct 12, 2026Citations rose from 21 to 22.
CitationsSep 25, 2026A first count of 1 influential citations.
CitationsSep 25, 2026A first count of 21 citations.
New paperSep 25, 2026Found by the weekly Hugging Face scan; not yet checked by hand.