Google DeepMind

Google DeepMind published a retrospective on 15 years of game-based AI research, tracing a line from 2015's Atari-playing DQN through AlphaGo, AlphaZero, MuZero, and AlphaStar to its current generalist agent, SIMA 2. The post also details a new research partnership with Fenris Creations, the studio behind EVE Online, to study continual learning, memory, and long-horizon planning in a persistent multiplayer universe.

Google DeepMind has released SL2T, a massively multilingual sign-language-to-text translation model trained on over 100,000 hours of data across 50+ sign languages. The model powers new sign-to-text dictation features in Gboard and Live Transcribe on Pixel 11, starting with American Sign Language to English.

Google DeepMind has introduced Gemini Robotics 2, a vision-language-action model it calls its most advanced yet, designed to control everything from tabletop robot arms to full-body humanoids. The company also released Gemini Robotics ER 2, an embodied reasoning model that replaces ER 1.6.

Google DeepMind has released Gemini Robotics ER 2, an embodied reasoning model that acts as a high-level planning brain for robots, adding continuous video progress tracking, precision moment-finding, and multi-robot collaboration. The model is available now via the Gemini API and Google AI Studio, with private preview access on the Gemini Enterprise Agent Platform.

Google DeepMind researchers developed GenCeption, which repurposes Alibaba's Wan2.1 video generator for computer vision tasks including depth estimation, segmentation, and 3D pose estimation. The model matches state-of-the-art specialized systems while training on only 7,500 synthetic videos—between 7 and 500 times less data than competing approaches.

Google has released Gemini 3.1 Flash Lite Image, also called Nano Banana 2 Lite, which the company describes as its fastest and cheapest image generation model. The model is available through Google's AI Studio and Gemini API with the identifier gemini-3.1-flash-lite-image.

Google DeepMind released Nano Banana 2 Lite (gemini-3.1-flash-lite-image), its fastest image generation model with 4-second text-to-image latency priced at $0.034 per 1K-resolution image. The company also opened developer access to Gemini Omni Flash (gemini-omni-flash-preview) for video generation and editing at $0.10 per second of output.

NVIDIA released a quantized version of Google DeepMind's DiffusionGemma 26B A4B IT, a multimodal model with 25.2B total parameters (3.8B active) that processes text, image, and video inputs. The NVFP4-quantized model achieves generation speeds exceeding 1,100 tokens per second on NVIDIA H100 GPUs while supporting a 256K token context window.

Amazon Web Services today announced availability of Google DeepMind's Gemma 4 family on Amazon Bedrock. The open-weight models include three instruction-tuned variants spanning 2.3B to 30.7B parameters, with 256K context windows, multimodal input support, and built-in reasoning mode.

Google DeepMind released DiffusionGemma, a 26B parameter mixture-of-experts model that generates text using discrete diffusion instead of autoregression. The model processes blocks of 256 tokens in parallel, achieving generation speeds exceeding 1100 tokens per second on H100 GPUs in low-batch settings.

Google DeepMind has released Gemma 4 12B, a 12-billion parameter multimodal model that runs locally on laptops with 16GB of RAM. The model eliminates separate vision and audio encoders, processing raw inputs directly through its language model backbone under an Apache 2.0 license.

Google DeepMind has released quantization-aware training (QAT) optimized versions of its Gemma 4 model family in GGUF Q4_0 format. The QAT versions preserve similar quality to bfloat16 while dramatically reducing memory requirements, with models available across the entire Gemma 4 lineup: E2B, E4B, 12B, 26B A4B, and 31B.

Google DeepMind released Gemma 4, a family of open-weight multimodal models ranging from 2.3B to 30.7B parameters. The flagship 12B Unified model eliminates separate encoders, processing text, images, audio, and video directly through a single decoder-only transformer with up to 256K token context window.

Google DeepMind has released Gemma 4 12B Unified, an encoder-free multimodal model that processes text, images, and audio through a single decoder-only transformer. The model features 11.95 billion parameters, a 256K token context window, and achieves 77.2% on MMLU Pro and 72.0% on LiveCodeBench v6.

Google DeepMind has integrated its Genie world model with Street View's 280 billion images spanning 110 countries, enabling users to explore AI-generated simulations of real locations. Waymo is already using Genie 3 to train self-driving cars on rare scenarios like tornadoes and unexpected obstacles.

Google DeepMind released the Gemma 4 E4B assistant model using Multi-Token Prediction (MTP) architecture that accelerates inference by up to 2x through speculative decoding. The 4.5B effective parameter model supports 128K context windows and handles text, image, and audio input with pricing not yet disclosed.

Google DeepMind has released a Multi-Token Prediction assistant model for Gemma 4 26B A4B that achieves up to 2x decoding speedup through speculative decoding. The model uses 3.8B active parameters from a 25.2B total parameter MoE architecture with 128 experts and a 256K token context window.

Google DeepMind has released Gemma 4, a family of open-weight multimodal models including a 31B dense model with 256K context window and four size variants ranging from 2.3B to 30.7B effective parameters. The release includes Multi-Token Prediction (MTP) draft models that achieve up to 2x decoding speedup through speculative decoding while maintaining identical output quality.

Google DeepMind launched Gemini 3.1 Flash TTS, a text-to-speech model that achieved an Elo score of 1,211 on the Artificial Analysis TTS leaderboard. The model introduces audio tags that allow developers to control vocal style, pace, and delivery through natural language commands embedded in text input, with support for 70+ languages.

Google DeepMind released Gemma 4, an open-weights multimodal model family in four sizes (2.3B to 31B parameters) with context windows up to 256K tokens. All models support text and image input, with audio native to E2B and E4B variants. The Gemma 4 31B dense model scores 85.2% on MMLU Pro, 89.2% on AIME 2026, and 80.0% on LiveCodeBench—significant improvements over Gemma 3.