Papersby GlyphGoogle DeepMind research papers
A company publishing mostly on foundation models, best known here for Gemini 1.5.12 papers · 9,143 citations · latest Jul 2026
DiffusionGemma 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 →Gemma 4 Technical ReportOpen multimodal models from 2.3B to 31B parameters, with a thinking mode and image and audio input.Google DeepMind356 citations81 upvotesRead →LiteFrame: 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 →Understanding 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 →LoGeR: 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 →SIMA 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 →Robot Learning from a Physical World ModelPhysWorld integrates video generation and physical world modeling to enable accurate robotic manipulation from visual demonstrations without real robot data.Google DeepMind23 citations32 upvotesRead →Vibe 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 →Video 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 →Do generative video models learn physical principles from watching videos?A new benchmark dataset, Physics-IQ, shows that current AI video models achieve visual realism without understanding underlying physical principles, highlighting the need for further advancements.Google DeepMind200 citations31 upvotesRead →Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextRecalls details across millions of tokens of text, hours of video and days of audio.Google DeepMind4,190 citations65 upvotesRead →Training Compute-Optimal Large Language ModelsChinchilla: for a fixed compute budget, train a smaller model on more data; parameters and tokens should grow together.Google DeepMind4,035 citations13 upvotesRead →