We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here: https://darioamodei.com/post/we-must-pace-the-frontier

We’re launching AlphaGenome Atlas: an AI-powered searchable database mapping the predicted impact of all 9 billion possible single-letter DNA changes. Here’s how it could help researchers better understand our biology 🧵

Banger paper from Google DeepMind and colleagues. (bookmark it) A model reads its entire KV cache on every generated token, even though it ends up attending to a tiny slice of it. In other words, if you ask about one detail from a 1M-token conversation the global attention layers re-read all of it, per token. The usual fix is to guess the relevant tokens first with cheap proxy scores, which still costs O(N) every step. Declarative Attention asks the model instead. The model declares where it needs to look, inside its own chain-of-thought. In this way, generation splits into three modes: global reads the full context, focus reads one specific region, and local reads only recent output. The inference engine parses those declarations the same way it parses tool calls and skips most of the cache read. On zero-shot on off-the-shelf weights across 15 long-context tasks, attended tokens during decoding drop 52.0% on Gemma-4-31B and 31.1% on Qwen-3.6-27B. Paper: https://arxiv.org/abs/2609.02737 Chat with Paper: https://academy.dair.ai/papers/language-models-can-control-their-own-attention-2609.02737

Impressive new paper from Google DeepMind. (bookmark it) It takes Co-Scientist out of simulation and into real-world experiments. A summary of the results: In computer science, it found an inference-time scaling architecture that beat six frontier models on HealthBench Hard and Professional under blinded physician review. The system designed a safe precursor route for MXenes and drove a semi-automated chemical vapor deposition reactor, producing a lamellar 2D material with structural similarities to the Ti3C2Tx lattice. It also tailored growth recipes to laboratory constraints in minutes, enabling single-attempt growth of monolayer MoS2, MoSe2, and WS2. In biology, it predicted E. coli swarming phenotypes across inducer gradients from sparse imaging data, matching unpublished real-world measurements. 30 domain experts wrote 450 reviews on end-to-end generated papers, and the reliability modules reduced hallucination and plagiarism. Paper: https://arxiv.org/abs/2608.26701 Chat with Paper: https://academy.dair.ai/papers/co-scientist-runs-closed-loop-experiments-in-real-labs-2608.26701

Games have been an important testbed for our AI research for over 15 years. 🎮 From mastering Atari to reaching Grandmaster in StarCraft II, they have driven some of our biggest AI breakthroughs. Our work with SIMA taught agents how to understand 3D worlds, but learning to navigate real human dynamics takes a living, persistent universe. Through our research partnership with @FenrisCreations, we’re exploring how to tackle open challenges in AI: 🔵 Continual learning to acquire new skills without forgetting past knowledge. 🔵 Deep memory systems that store and retrieve information far beyond today’s context windows. 🔵 Long-horizon planning over weeks, months, or years. 🔵 Multi-agent dynamics spanning cooperation, negotiation, economics, and emergent behaviors. Our long-term goal is to use AI to discover entirely new gameplay experiences in partnership with game developers – making games more accessible and personalized – while applying what we've learned to problems in the real world and scientific discovery. Find out more → https://goo.gle/4xcwzOH