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Google / Gemini Latest

Gemini models from Google and DeepMind, Veo video generation, frontier research and AI updates across Google's products.

22 picked21 in the last 30 days100 collected

November 13 02:52 updated

Google / Gemini

TodayNovember 13FridayItems 1–20 of 22
  1. Google DeepMind90

    Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows

    Google DeepMind introduced Gemini 4 Hydrogen, a Gemini 4 model built for fast everyday coding and agentic execution rather than the long-horizon reasoning of Gemini 4 Carbon and Argon. It scores 51 on the Artificial Analysis Intelligence Index at its default high thinking level versus 50 for Argon, reaches 54 at a new xhigh setting, and is priced flat at $1.50 per million input tokens and $7.50 per million output tokens across its 1 million-token context window. Google also launched Orchestrator in Google Antigravity, which switches between models like Carbon and Hydrogen within a session and reached near-Carbon task success on Terminal-Bench 4.0 while using 24% less quota and finishing 19% faster.

    Why it matters: Google's Gemini 4 Hydrogen targets fast everyday coding and agentic work, with flat $1.50/$7.50 per million token pricing across a 1M context window and a new Orchestrator that switches between Gemini 4 models.

  2. Google Blog · AI82

    Google Search upgrades globally to Gemini 4 Hydrogen

    Google Search has moved all users worldwide to Gemini 4 Hydrogen, which the company calls its biggest model upgrade to Search yet. The model improves intent understanding for multi-part and typo-heavy questions, adds deeper instruction following and tool calling, and strengthens query fan-out to surface links users might not otherwise find. Responses also become more visual, with more images, comparison tables and custom tools, across all supported AI Mode languages.

    Why it matters: Google Search's largest model upgrade to date, with Gemini 4 Hydrogen now handling multi-part questions, tool calling and query fan-out for all users globally.

November 12Thursday
  1. The Decoder88

    Google DeepMind's AlphaProof Code verifies Signal's cryptographic core in four weeks instead of years

    Google DeepMind unveiled AlphaProof Code, an AI system that mathematically proves software meets its security specifications, and used it to verify the cryptographic core code running on Signal users' devices in four weeks, work that typically takes years. After experts defined formal security requirements, AI agents built on Gemini Carbon wrote over six million lines in the Lean proof language and flagged roughly one percent of the codebase for human review, catching two deliberately planted bugs immediately. Signal CTO Ehren Kret said the approach strengthens trust in data privacy as AI threats grow, and DeepMind plans to extend the method to power grids and medical devices through systems like CodeMender.

    Why it matters: DeepMind's AlphaProof Code verified Signal's real cryptographic code in four weeks, showing how AI agents can move formal verification from design documents to production software.

  2. Google Blog · AI78

    Google launches Gemini Decisions API and Gemini Decision 1 model for fast closed-set decisions

    Google introduced the Gemini Decisions API, powered by the new Gemini Decision 1 model (gemini-decision-1) and available through the Gemini API in Google AI Studio. A single multimodal input of up to 256K tokens can be checked against up to 1,000 yes/no, multiple-choice and rubric-scoring questions in one forward pass, returning probabilities and confidence scores at 150-250 ms end-to-end latency for $0.10 per 1M input tokens with no output-token charges. Google says the model leads its benchmark suite with the lowest calibration error and is nearly 5x cheaper than Gemini 3.5 Flash-Lite with the Interactions API; internal uses include model routing in Gemini Notebook, wireframe generation in Stitch and adversarial-content checks for MCP tools in Search.

    Why it matters: Google's new endpoint scores up to 1,000 multimodal yes/no, choice and rubric questions in one pass at 150-250 ms, giving agent routing and policy checks a cheaper alternative to generative calls.

November 11Wednesday
  1. Google DeepMind72

    DeepMind releases AlphaGenome 2, a DNA model predicting variant effects in specific cell types

    Google DeepMind announced AlphaGenome 2, a next-generation DNA sequence model that predicts how DNA sequences and their variants affect specific human cell types. Trained on more than 107 million individual cell profiles across 68 tissues, the conditional model identified nearly twice as many expression-changing variants as the previous model, and on the TenK10K immune cell dataset it recovered 30% of causal variants versus 16% for the original AlphaGenome fine-tuned on single-cell data. It is available for non-commercial research via the AlphaGenome API and in private preview for commercial research.

    Why it matters: AlphaGenome 2 shifts from bulk-tissue averages to single-cell conditioning, roughly doubling causal variant recall on immune cell data, which matters for linking GWAS signals to specific cell types.

  2. Google DeepMind88

    Google DeepMind releases Gemini 4 Carbon for agentic coding, cybersecurity and scientific reasoning

    Google DeepMind made Gemini 4 Carbon available to developers, enterprises, scientists and consumers, positioning it above Gemini 4 Argon for tasks that need deeper reasoning and more tokens. Carbon ranks #1 on CursorBench 4.0, Perplexity's WANDR and Zapier's AutomationBench at more than 2X lower cost per task than the next best external model, and is priced at $2 per million input tokens and $10 per million output tokens, the same introductory price as Argon. Google also previewed /swarm in Google Antigravity, which turns a single prompt into a coordinated team of agents, and reported that Carbon blocked 99% of attack trajectories in simulated cyberattack testing.

    Why it matters: Google's release notes detail how Gemini 4 Carbon trades higher token usage for deeper reasoning, ranking first on CursorBench 4.0, WANDR and AutomationBench at under half the per-task cost of the next external model.

  3. Google Developers Blog75

    Google releases A2UI 1.0, a stable open protocol for agent-generated native UI

    Google released A2UI 1.0, the first stable version of its open protocol that lets AI agents answer with interactive screens rendered by each app's own native components. Version 1.0 adds bidirectional function calls, accessibility support and extension metadata, plus open-source renderers for React, Angular, Lit, Flutter, SwiftUI and Jetpack Compose (the Compose renderer is at 0.9.1 with 1.0 coming soon) and agent SDKs for Python, TypeScript and Dart. Atlassian is bringing A2UI to Jira, Alation renders its feeds with it in Claude, and ByteDance shipped a Lynx renderer in June 2026 that supports 0.9 today.

    Why it matters: Google's stable A2UI 1.0 protocol lets agents return native interactive screens instead of text walls, with renderers for web, Android and iOS and early adopters including Atlassian and ByteDance.

November 10Tuesday
  1. Google DeepMind82

    Google DeepMind introduces AlphaProof Code, formally verifying Signal's cryptographic code

    Google DeepMind researchers used AlphaProof Code, a multi-LLM agentic framework, to formally verify key security properties of the actual code underlying the Signal protocol. Agents powered by Gemini Carbon generated over six million lines of Lean, and human experts reviewed only about one percent of the codebase; the verification took four weeks, a task that historically takes expert teams years. When two previously fixed bugs were reintroduced into an isolated version of Signal's code, the system produced machine-checked proofs that the buggy versions were insecure, and DeepMind is open-sourcing its formal verification artifacts.

    Why it matters: Google DeepMind's multi-agent framework formally verified Signal's deployed cryptographic code in four weeks, a task that historically takes expert teams years.

  2. Google DeepMind78

    Google DeepMind releases Gemini Robotics 2.5 VLA model for real-world dexterity

    Google DeepMind introduced Gemini Robotics 2.5, its most capable vision-language-action model to date, which expands the range of skills a robot can learn and speeds up how fast it acquires them. Evaluated across 200 distinct tasks, the model achieves a 30% to 100% higher success rate than previous baselines while completing tasks 20% to 30% faster, and in a Mercedes-Benz production-line proof-of-concept, Apptronik's Apollo 2 humanoid picked and packed automotive parts with a 96% success rate. The release also adds inference-time reasoning guidance for physical safety, which raised safe task completion by 33% around obstacles and 43% around nearby people, plus ASIMOV-Jailbreak, an open-source benchmark on which Gemini 4 Argon reaches 99.5% jailbreak resistance.

    Why it matters: Gemini Robotics 2.5 improved its success rate on 200 tasks by 30% to 100%, and achieved a 96% pick-and-place success rate in a pilot on the Mercedes-Benz production line, providing reference data for evaluating the real-world deployment of VLA models.

  3. Google Developers Blog72

    Google AI Edge Gallery adds on-device speech recognition and text-to-speech

    Google introduced on-device Automatic Speech Recognition and Text-to-Speech in Google AI Edge Gallery, letting apps run multilingual ASR and expressive TTS fully offline alongside on-device SLMs like Gemini Nano and Gemma 4. On Android, ML Kit's new speech models transcribe 100+ languages at 10x real-time speed with an 8.0% to 9.0% Word Error Rate on FLEURS, while the few-step diffusion TTS starts speaking in about 100 ms and generates 24 kHz audio at up to 50x real-time speed. Cross-platform support comes through MediaPipe Tasks and LiteRT with open-weight bundles including Parakeet-TDT 0.6B, PocketTTS 100M, Kokoro-82M and Phonon 31M, demonstrated in the XR Language Teacher showcase and voice-enabled AI Chat.

    Why it matters: Google adds fully offline ASR and TTS to AI Edge Gallery, with ML Kit reporting 100+ language transcription at 10x real time and a diffusion TTS that starts speaking in about 100 ms.

November 9Monday
  1. Google Developers Blog60

    Google Cloud MaxText adds context parallelism for long-context training on TPUs

    Google Cloud released long-context training support in MaxText v0.2.4, its open-source JAX training framework, adding four context-parallel strategies on Cloud TPU: All-gather, A2A (DeepSpeed Ulysses), P2P (Ring Attention) and the A2A + P2P hybrid (USP). Benchmarks with Llama 3.1 8B reached 70% MFU on TPU 5p at sequence lengths up to 4 million tokens, and context extended to 14 million tokens on both TPU 5p and TPU 7, with TPU 7 holding 50% MFU below 6 million tokens. The release is available on PyPI, in Docker images and on GitHub main, with sample recipes for TPU 7 and TPU 5p.

    Why it matters: MaxText's four context-parallel strategies on Cloud TPU, with benchmark MFU figures for Llama 3.1 8B, give teams a concrete reference for choosing how to train at multi-million-token sequence lengths.

  2. Google Developers Blog62

    Google makes Pathways generally available on Google Cloud for large-model training

    Google announced the General Availability of Pathways, its distributed training system for large models including Gemini, on Google Cloud Platform. Pathways replaces lockstep SPMD execution with a single Python client that declares the whole computation as a dataflow graph, while a distributed runtime places blocks on virtual devices, moves and reshards tensors, and orders work across the cluster. Google says the design lets a failed host be remapped onto healthy chips instead of restarting the job, and that a shared Pathways service can serve several clients from one cluster.

    Why it matters: Google explains how Pathways' single-controller model differs from SPMD and how it handles cross-group tensor transfer, scheduling and host failure recovery.

November 6Friday
  1. Google DeepMind78

    Google DeepMind releases Gemini Omni 1.2 Flash with 15-second clips and 30% lower price

    Google DeepMind rolled out Gemini Omni 1.2 Flash, now generally available across the Gemini API and Gemini for business, with native 15-second video generation, multi-turn conversational editing and stronger reference following. The price drops 30% from Gemini Omni 1.1 to $0.07 per second for 720p video, and users can draft at 360p or 720p before upscaling to 1080p or 4K. Clips can be set from 3 to 15 seconds in one-second increments, and outputs carry an imperceptible SynthID watermark.

    Why it matters: Gemini Omni 1.2 Flash raises native clip length to 15 seconds and cuts the price 30% to $0.07 per second of 720p video, with multi-turn conversational editing now generally available.

  2. Google Blog · AI80

    Google releases Gemini Omni 1.2 Flash with 15-second clips and a 30% price cut

    Google DeepMind rolled out Gemini Omni 1.2 Flash and made it generally available across the Gemini API and Gemini for business. The model generates and edits native clips up to 15 seconds, up from 10 seconds in Omni 1.1, supports multi-turn conversational editing, and holds character, product, and style references more consistently; 720p video now costs $0.07 per second, 30% less than Omni 1.1. Outputs carry a SynthID watermark, and the model is rolling out in Flow, Google Vids, the Gemini app, and Google Ads.

    Why it matters: Gemini Omni 1.2 Flash cuts the price 30% to $0.07 per second for 720p video while extending native clips to 15 seconds, giving teams producing video at scale a cheaper option to compare against prior versions.

  3. Google Research72

    Google researchers detect hypertension signs with smartwatch PPG and accelerometer data, no cuff calibration needed

    Google Research published a Nature study on AI-PPG-ACC-HTN, a multimodal system that detects signs of hypertension from smartwatch PPG and accelerometer data without cuff calibration. Validated against 24-hour ambulatory blood pressure monitoring in 196 participants across five US sites, it reached 65.8% sensitivity and 90.0% specificity for initial screening, and 78.4% sensitivity and 90.9% specificity for confirmatory testing. The authors describe the work as early feasibility research, not a replacement for clinical arm cuffs or a standalone diagnosis.

    Why it matters: Google's cuffless smartwatch hypertension model is benchmarked against 24-hour ambulatory monitoring, showing how passive multi-day sensor aggregation could support opportunistic screening.

  4. Google Developers Blog60

    Google study finds stale TODO bug references distract coding agents in 13% of sessions

    ) references. Agents acted on the bug ID in 6% of encounters, and 83% of those actions were unprompted by the developer; 41% of unprompted detours chased already closed tickets while 57% hit still-open bugs and expanded scope. Controlling for developer, codebase complexity, task type, model tier and other factors, unprompted bug detours added 70% more steps, 77% more tokens and 105% more wait time per session.

    Why it matters: Google's large-scale trace analysis shows stale TODO bug references pull coding agents into unprompted detours, and its cleanup agents removed thousands of obsolete references with over 95% reviewer acceptance.

November 4Wednesday
  1. Google DeepMind72

    Google Antigravity adds Cheminformatics Skills for agentic drug discovery

    Google DeepMind introduced Cheminformatics Skills for Google Antigravity, letting an agent search literature, curate compounds, run matched molecular pair analysis, train potency models and propose rigidified macrocycles with synthesis routes in about an hour, a workflow it says previously took multiple days. The skills are grounded in RDKit with connectors to ChEMBL and PubChem, and the team demonstrated them on crizotinib and lorlatinib analogues for non-small cell lung cancer. The skills are available today via the Antigravity app's Science plugin, and DeepMind is offering 10,000 scientists the highest usage tier free for 6 months, subject to eligibility, capacity and selection.

    Why it matters: Google DeepMind shows its Cheminformatics Skills in Antigravity compressing a multi-day drug discovery loop into about an hour, while noting that designs remain hypotheses needing lab validation.

October 29Thursday
  1. Google DeepMind82

    Google DeepMind announces Gemini 4 Carbon, a frontier model focused on cybersecurity and long-horizon tasks

    Google DeepMind announced Gemini 4 Carbon, its strongest model yet, with state-of-the-art cybersecurity and long-horizon capabilities, ranking #1 on the v5.0 Artificial Analysis Intelligence Index at 59. It scores 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation and 71.0% on CWE-bench for patching, and reaches #4 (1768) on WebDev Arena. Carbon launches at the same introductory price as Argon, $2 per million input tokens and $10 per million output tokens, and is first available to a subset of cyber defenders through the Fairwind Program before wider release.

    Why it matters: Gemini 4 Carbon's benchmark results in vulnerability discovery and patching show how frontier models are being positioned for cybersecurity defense work.

October 28Wednesday
  1. Artificial Analysis Articles82

    Gemini 4 Carbon tops Artificial Analysis Intelligence Index v5, ranks second on Coding Agent Index

    Google's Gemini 4 Carbon is the new No. 5 on the Artificial Analysis Coding Agent Index. 5 (max, 62), and it leads 4 of the 9 Index evaluations including HLE-Diamond and Terminal-Bench-Science. 1 rises from 4% to 11%; the model is being rolled out to selected users and is not yet publicly available.

    Why it matters: Gemini 4 Carbon tops Artificial Analysis Intelligence Index v5 at 59 and trails Claude Opus 5.5 by 3 points on the Coding Agent Index, with per-task cost and hallucination figures for comparison.

October 20Tuesday
  1. Google Developers Blog60

    Google 将 TorchTitan 接入 TorchTPU，并发布 TPU 模型优化手册

    Google released a TPU fork of Meta's TorchTitan that runs PyTorch training on Cloud TPUs through TorchTPU's native torch.device("tpu") backend, with TPU-native kernels and a step-by-step optimization playbook. On TPU v7x (Ironwood), Llama 3 405B holds about 57% MFU from 128 to 1,024 chips, Qwen3 235B reaches 32.42% MFU on one 1,024-chip slice, and FLUX.1 reaches 19.4% MFU on 1,024 chips at 93% weak-scaling efficiency. The fork is at https://github.com/google-pytorch/torchtitan/, and a Transformers modeling backend plus an agent onboarding skill extend the same trainer to other checkpoints.

    Why it matters: Google 与 Meta 合作把 TorchTitan 移植到 TPU，并给出从 FLUX.1 到 Qwen3 235B 的逐级调优路径与 MFU 数字，可作为 PyTorch 团队评估 TPU 训练可行性的参考。