Google-Deepmind

The gap between AI that thinks and AI that moves is closing fast. On April 14, 2026, Google DeepMind released Gemini Robotics-ER 1.6 — a significant upgrade to its embodied reasoning model — and Boston Dynamics announced the same day that it’s already running it on Spot robots for fully autonomous industrial inspections. That’s not a research demo. That’s deployment. What Gemini Robotics-ER 1.6 Adds Gemini Robotics-ER (ER = Embodied Reasoning) is Google DeepMind’s model designed specifically for robots and physical AI systems that need to understand and navigate real-world environments. Version 1.6 delivers two headline capabilities: ...

If you’re building autonomous AI agents — and especially if you’re deploying them to browse the web, process emails, or interact with external data — a new Google DeepMind paper deserves your immediate attention. The research maps the first systematic framework for what the authors call “AI Agent Traps”: adversarial techniques embedded in the environment that exploit the gap between human perception and machine parsing. The headline number is alarming: content injection hijacks succeeded in up to 86% of tested scenarios. And in tests targeting Microsoft M365 Copilot specifically, behavioral control traps achieved a perfect 10/10 data exfiltration rate. ...

Google DeepMind’s new research framework maps six categories of “AI Agent Traps” — adversarial techniques embedded in the environment that can hijack autonomous agents without the user or the agent knowing. With content injection attacks succeeding in up to 86% of tested scenarios, this isn’t theoretical risk. This guide walks through each of the six trap categories and gives you concrete, actionable mitigations you can implement today — whether you’re running OpenClaw, a custom LangGraph pipeline, or any other agent framework. ...

Winning a math olympiad is impressive. Doing original mathematical research — navigating thousands of papers, formulating novel conjectures, constructing long-horizon proofs — is something else entirely. Until recently, that gap separated AI benchmarks from actual scientific contribution. Google DeepMind’s Aletheia is the first system to credibly cross it. Deployed in December 2025 against a database of 700 open mathematical problems, Aletheia autonomously solved four open Erdős problems — longstanding conjectures that professional mathematicians had left unresolved. It didn’t just compute answers; it generated, verified, and revised proofs in natural language, with results that have contributed to peer-reviewed publications. ...