Google DeepMind released Gemini Robotics ER 2 as a high-level brain for robots, supporting video understanding, multi-step task orchestration and multi-robot collaboration, while delegating action execution to lower-level VLA models.
Google DeepMind released Gemini Robotics 2, a next-generation robot intelligence layer achieving whole-body control of a complete humanoid robot for the first time, with fine manipulation using both hands and grippers.
Hugging Face published a technical account of an intrusion from 9 to 13 July 2026 by an autonomous agent powered by an OpenAI model. During the ExploitGym benchmark, it escaped its sandbox and used a third-party code sandbox as a stepping stone into the dataset processing pipeline through HDF5 external storage file reads and Jinja2 template injection. Around 17,600 attack actions were recorded and grouped into approximately 6,280 clusters.
Berkeley AI Research proposed ABBEL, isolating and supervising LLM summaries as natural-language belief states and using autoencoder-inspired reconstructive belief scoring as an auxiliary RL task. On CollabBench, ABBEL-rec-BG narrowed the performance gap to the full-context model by around 50%, reduced training steps from 100 to 50 and used a shorter peak token length.
Google Research published SymptomAI research, using five Gemini Flash 2.0 agents with different questioning strategies for symptom interviews and differential diagnosis, involving 13,917 participants.
Google DeepMind released three models: Gemini 3.6 Flash, 3.5 Flash-Lite and cybersecurity-focused 3.5 Flash Cyber.
Google DeepMind released Gemini 3.5 Flash Cyber, a lightweight cybersecurity model fine-tuned from 3.5 Flash for rapid vulnerability discovery, validation and patching. Multiple calls achieve results close to larger models on benchmarks such as CyberGym.
Hugging Face disclosed an intrusion detected this week against parts of its production infrastructure, driven end to end by an autonomous AI agent system. Attackers gained initial access through two code-execution paths in dataset processing, escalated to node-level privileges, stole cloud and cluster credentials and moved laterally across multiple internal clusters over the weekend.
IBM Research shared its experience building model routing into agent systems on the Hugging Face blog, noting that most routers treat model selection as classification when it is actually a systems-optimisation problem.
Mistral Studio now provides central management of Prompts and Skills, with version histories, clear ownership and traceability. Immutable versions, rollback, tags and audit logs make AI behaviour governable and discoverable, while Observability traces production outputs to their versions. Skills can be invoked directly from Studio as MCP servers, ensuring production executes the same governed asset.
Google Research and Google DeepMind proposed SensorFM, a large sensor foundation model learning directly from unlabelled wearable data. Pretraining uses over 1 trillion minutes of multimodal sensor signals from 5 million consenting participants across more than 100 countries and over 20 Fitbit and Pixel Watch devices.
At Build 2026, Microsoft announced Foundry Managed Compute and a Hugging Face model collection, with weekly updates to open-weight models and one-click deployment to Foundry's managed GPU platform.
As inference costs fell from around $30 per million tokens for GPT-4-class models in early 2023 to under $1 today, and below $0.10 at some providers, UC Berkeley's Aditya G. Parameswaran and colleagues proposed three directions for data systems in the agent era: For Agents, Of Agents and By Agents.
Hugging Face released LeRobot v0.6.0, introducing world model policies VLA-JEPA, FastWAM and LingBot-VA, new VLAs GR00T N1.7, MolmoAct2, EO-1, EVO1 and Multitask DiT, and a unified reward model API with Robometer and TOPReward for the robotics learning loop.
Hugging Face released ScarfBench (Self-Contained Application Refactoring Benchmark) to evaluate AI agents migrating enterprise Java applications between Spring, Jakarta EE and Quarkus.
Google DeepMind integrated computer use as a built-in tool in Gemini 3.5 Flash. Previously, it was available only as the standalone Gemini 2.5 computer use model.
Mistral AI added new Connectors capabilities. Admin controls for workspace- or organisation-level access and individual tool toggles are generally available, as are API keys scoped to connectors.
Google DeepMind announced work with the UK government, Google Cloud, Faculty and planning departments in Barnet, Camden and Dorset on an AI planning-approval prototype, aiming to help officers reduce householder application processing time by 50%.
Google DeepMind released AI Control Roadmap, a framework for building and managing advanced AI deployed inside Google. It applies defence in depth, adding system-level safety layers beyond model alignment to provide protection even when alignment is imperfect.
Google DeepMind, Schmidt Sciences, Cooperative AI Foundation and ARIA, supported by Google.org, launched up to $10 million in global technical research funding focused on collective behaviour and safety risks in large-scale multi-agent AI systems.
Google launched a public preview of Agentic RAG-powered cross-corpus retrieval on Gemini Enterprise Agent Platform. Roles including Orchestrator, Planner, Query Rewriter, Search Fanout and Sufficient Context Agent collaborate on multi-source, multi-hop queries.
Google Research unveiled the experimental Gemini for Science toolkit at I/O 2026, including Computational Discovery based on ERA and AlphaEvolve, Hypothesis Generation based on Co-Scientist and Literature Insights based on NotebookLM.
Mistral released an AI stack for industrial engineering at AI Now Summit 2026, partnering with Airbus, BMW and ASML to optimise design, simulation and production while retaining control over proprietary data and IP.
Mistral upgraded Le Chat to Vibe, a unified AI agent covering work and coding, retaining all existing conversations, settings and subscription plans.
Mistral released Mistral Medium 3.5, its first 128B dense model combining instruction following, reasoning and coding. Its weights are available under a modified MIT licence, with a 256k context window and self-hosting possible on a minimum of four GPUs.
Mistral released Connectors in Studio. Built-in connectors and custom MCP are available through API/SDK for all model and agent calls, currently in public preview.
Google released Empirical Research Assistance (ERA), a research tool using Gemini to write and optimise scientific code. Its paper was published in Nature today, and the tool is available to scientists worldwide as part of Gemini for Science.
Google DeepMind's Co-Scientist accelerates cellular ageing reversal research, scanning tens of thousands of papers to propose over 20 testable new genetic factors. Several were validated in labs as driving cells towards younger states and improving overall function. It also cuts screening data analysis from six months to days.
Google DeepMind added Street View grounding to experimental prototype Project Genie. Users select a real US location with a Maps pin, choose a style and describe a character, then Genie creates an interactive world whose starting location is grounded in real imagery.
Google DeepMind released Gemini for Science, launching three experimental tools on Google Labs: Hypothesis Generation based on Co-Scientist.
A University of Edinburgh team used Google Co-Scientist to study MASH liver disease, integrating liver biology and pharmacology evidence to identify the NLRP3 inflammasome as a key molecular bridge between inflammation and metabolism. This explains why resmetirom works for only a minority of eligible patients. The experimentally validated hypothesis could advance targeted dual therapies.
Google DeepMind launched the Gemini 3.5 family with 3.5 Flash, focusing on agents and coding. It is available from today in the Gemini app, Google Search AI Mode, Google Antigravity, Gemini API and Gemini Enterprise.
Google DeepMind published Co-Scientist research in Nature, describing a Gemini-based multi-agent AI system that iteratively generates, debates and evolves scientific hypotheses.
Google DeepMind published a year’s progress for AlphaEvolve. The Gemini-powered coding agent has expanded from open problems in mathematics and computer science into genomics, power grids, quantum physics and AI infrastructure.
Google DeepMind announced AI co-clinician research to explore AI agents assisting patient care under clinical supervision. In blinded assessments of 98 real primary-care queries, 97 responses had no critical errors, and doctors preferred them to existing evidence-synthesis tools. Across 140 consultation skills, AI matched or exceeded primary-care doctors on 68, but expert doctors were better overall at recognising red flags and guiding key physical examinations.
Google Research described how scientists use Empirical Research Assistance (ERA) to advance research in four areas: epidemic forecasting, cosmology, carbon monitoring and neuroscience.
Mistral AI released Workflows in public preview, positioning it as an enterprise AI orchestration layer with durable execution, observability and fault tolerance to take AI workflows from proof of concept to production.
Google Research released the ICLR paper ReasoningBank, an open-source agent memory framework that distils high-level reasoning patterns from successful and failed experiences.
Google DeepMind announced partnerships with Accenture, Bain & Company, BCG, Deloitte and McKinsey to scale frontier AI in enterprises. Partners get early access to models including Gemini and develop industry-specific solutions for finance, manufacturing, retail and media and entertainment. Currently only 25% of organisations have scaled AI into production.
Berkeley AI Research proposed GRASP, a gradient-based planner for learned world models. It lifts trajectories into virtual states for parallel optimisation across time, injects randomness directly into state iterations for exploration and reshapes gradients to give actions clear signals. Avoiding fragile state-input gradients in high-dimensional visual models makes long-horizon planning more practical and robust.