I’m building a new team at @GoogleDeepMind to work on Open-Ended Discovery! We’re looking for strong Research Scientists and Research Engineers to help us push the frontier of autonomously discovering novel artifacts such as new knowledge, capabilities, or algorithms, in an open-ended self-improving loop. We aim to work on ambitious research projects in a fast-paced manner. If this sounds appealing to you, apply using the link below by Friday, August 1st EOD: job-boards.greenhouse.io/dee…

We are again recruiting @bold_lab_ai - please share the love 🙏. We are looking for: 1) Postdocs (my.corehr.com/pls/uoxrecruit…) -- deadline 9th of Oct at noon 2) research assistants (my.corehr.com/pls/uoxrecruit…) -- deadline 9th of Oct at noon 3) Strategic Partnership Project Manager (my.corehr.com/pls/uoxrecruit…) -- deadline 14th of Oct at noon

Great collection of stories showing how AI "finds a way" to outwit us! Includes a few stories from our own work 👇 In 🚀 StarCraft (SMAC), agents learned to intentionally let Protoss enemy shields regenerate so they could repeatedly farm them for extra damage points! 🛡️🔄 In 🌈 Rainbow Teaming , an evolutionary search designed to find LLM jailbreaks got so creative it accidentally jailbroke the safety evaluator itself, fooling it into thinking safe text was unsafe! 🎯🪤

this is why I have been saying for years unsupervised environment design is an AI safety research agenda if an agent is a reflection of its training environments, we should be careful about how we design those environments!

Amazing to see all the excitement around RSI, which finally feels within reach.  I pitched RSI back when I was at FAIR in 2023, right after we did Toolformer, which was arguably too early. But we got to see the first signs it may be possible while working on MLGym with @deepaknathani11. I shared my vision for how we can achieve RSI and Superhuman Scientific Discovery in my @raais talk earlier this year.  Spoiler alert: open-endedness is a key piece of the puzzle.

We have had a few questions about our perfect applicant, so here are a few made up examples: You graduated from a Phd in machine learning a couple of years ago and have been doing great work at one of the frontier labs, pushing the limits of the current paradigm. Now you think it's time to aim higher. We want you to apply. You are about to graduate from a stellar Phd in machine learning during which you have made contributions that have started to shape the field. We want you to apply. You graduated from a stellar Phd in machine learning and have been doing a postdoc since. We want you to apply. You are an academic in machine learning, leading your own group but have been wondering whether this really is the right setup to make a difference. We want you to apply. You are brilliant and a great team player. You focus on creating exponentially large pies rather than fighting over the size of your slice. We want you to apply.

Unique opportunity to do groundbreaking AI research in a vibrant academic lab with tons of freedom, compute, and ambition. Be BOLD and apply! 🦋

Combining LLMs with Bayesian methods could provide a path towards more efficient and principled automation of scientific discovery. Nice to see these explorations.

Great to see more work on improving models at replicating research.

Our new work, The AI Picbreeder Experiment, explores the use of frontier models as drivers of synthetic open-endedness. If we're serious about putting these things in the driver's seat of a new and automatic science, then we need to know what they're really made of in terms of the ability to create and discover through intuition. Giving shape to the formless, making decisions based on vibes, having "taste"—whatever you want to call it—Can they do it? Do they have the sauce? Picbreeder, a website where human users collaborated to spontaneously evolve images, is just the sauce-bearing test we need. Here, images were represented as neural networks that could be bred and mutated, with humans playing the role of natural selectors. By design, this interface prohibits the creative baggage of premeditation, of having goals in advance, and demands the artist patiently follow the flow of the work and seize upon serendipitous opportunities when they arise. It's more like catching fish from a stream than drawing a picture. And yet, distributing their work across many sessions, and branching and remixing each other's creations, humans were ultimately able to bend these neural networks into all manner of interesting, evocative, and striking images. So, can large vision language models do the same? On the blog, we've built an interactive archive viewer that allows visitors to walk through galleries of Picbreeder images created by both humans and AI, and judge for themselves. Call us old fashioned, but we're pretty sure the human output has something special that the AI can't quite yet replicate. We design a number of evaluation metrics to get at this quality. We ask: "How visually different are the images in the archive? How much do they look like real things? How different are the things they look like?" The numbers show the humans coming out on top. And looking at the AI-generated archives and lineages, we find traces of an anxious attachment to plans and objectives. Often, even when the AI makes an apparent creative leap—e.g. transforming an image of a hood ornament into a side view of a car—it really stays stuck in place in some broader semantic/thematic space. And that's to say nothing of the handful of archives littered almost entirely with top-down views of soda can pull tabs, or high frequency circular patterns that appear chaotic and uninteresting to us, but apparently scratch some perceptual itch in the agents. And yet we're optimistic. Though the AI's output is less refined, its movement through the stream of images less graceful and vivacious than our own, what we have here is a plausible model organism of open-endedness. The agents indeed (re)discover distributions of novel and interesting images when left to their own devices. They display a keen eye (even sometimes discovering optical illusions that might slip by a casual glance from a human), and explore persistently under considerable creative constraints. This allows us to model factors that are consequential to such open-ended exploration; i.e. injecting noise into the agents' decision making process, playing with their memory, and seeding them with subtly distinct personalities—all of which can be beneficial in the right doses. And there's something to be said for searching without objectives. Prior work shows that if we optimize Picbreeder's pattern-producing neural networks to resemble a particular image (say, a skull), these representations will be fractured (meddling with their internal weights will immediately explode the skull beyond recognition), while the same neural image found by humans via open-ended exploration is robust to such perturbations, and even shows meaningful variations across them (e.g. the jaw opening and closing). Our VLM agents also stumbled upon images of skulls. Their representations are not as neatly semantically factorized as those discovered by humans, but neither are they nearly as fractured as those discovered by optimization. This suggests that if we want to have AI build the next generation of AI, then it will be crucial to let them attack this problem through aimless wandering. Without this freedom, future models will be brittle and myopic; with it, they will have developed a more thorough model of the world, and an improved capacity for the kind of creative insight that is so quietly fundamental to the most meaningful of human endeavors.

Good summary of my recent talk from @richardcsuwandi 🙏

My @raais talk on the path towards Superhuman Scientific Discovery is now available on youtube.

Frontier AI, trained on vast human knowledge, could unlock novel discoveries by connecting disparate fields. Humans struggle to master more than one. AI + Human synergy promises accelerated, superhuman breakthroughs. Watch @robertarail of @googledeepmind at @raais 2026

Excited to give a talk at the @icmlconf RLxF Workshop today at 3:30pm. I’ll be talking about the role of open-ended esss in achieving superhuman scientific discovery. Thank you @shaohua0116 @shaneguML et al. for organizing this!

I’m hiring! Come join our team at Google DeepMind in London or Mountain View to work on Gemini agent post-training. We are looking for Research Scientists and Research Engineers interested in advancing the capabilities of AI agents. Please apply here: google.com/about/careers/app…

Come chat with some BOLD people at @icmlconf 🦋

Come find me tomorrow, Tuesday, from 10:30-12:15. Hall A #1803! Hope to see you there :)

I'm hiring for my team at GDM! If you’re passionate about engineering scalable agentic learning systems and doing impactful research, please apply here: goo.gle/4wsFoTU

We hope you’ve enjoyed a sneak peek of work from BOLD and our collaborators at #ICML2026! See below for a full summary of where you can find us this week: ▶️ (Poster) Procedural Generation of Algorithm Discovery Tasks in Machine Learning, Hall A #1803, Tuesday 10:30 - 12:15, led by @AlexDGoldie ▶️ (Poster) h1: Bootstrapping LLMs to Reason over Longer Horizons via Reinforcement Learning, Hall A #2704, led by Alesia Ivanova @sumeetrm ▶️ (Poster) Goal-Conditioned Agents that Learn Everything All at Once, Hall A #310, Tuesday 14:00 - 15:45, led by @mitrma ▶️ (Poster) Rubric Curriculum RL: Exploiting the Generation-Verification Gap in Creative Writing, Hall A #2605, Wednesday 14:30 - 14:15, led by Tejas Krishnan @sumeetrm ▶️ (Poster) Evolution Strategies at the Hyperscale, Hall A #3712, Thursday 10:30 - 12:15, led by @bidiptas13 @JuanDuquevan Mattie Fellows ▶️ (Poster) Dreaming in Code for Curriculum Learning in Open-Ended Worlds, Hall A #213, Wednesday 17:00 - 18:45, led by @k_mitsides ▶️ (Poster) Evolution Strategies at the Hyperscale, Hall A #3712, Thursday 10:30 - 12:15, led by @bidiptas13 @JuanDuquevan Mattie Fellows ▶️ (Poster) The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind, Hall A #3504, Thursday 14:30 - 16:15, led by @_andreilupu ▶️ (Poster) LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning, Hall A #1705, Thursday 14:30 - 16:15, led by @sumeetrm @DanielNichols10 @CharlieLondon02 Peggy Li Fabio Pizzati ▶️ (Talk) Superhuman Scientific Discovery, RLxF Worskhop, Friday 15:30 - 16:00, by @robertarail ▶️ (Panel) RLxF Worskhop, Friday 16:00 - 17:00, by @robertarail ▶️ (Workshop) Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs, FoGen Workshop, Friday, led by @jakobhartmann99 James Harvey Jhonathan Navott ▶️ (Workshop) Elicitation Format Drives Divergent LLM Geopolitical Forecasts, AI Forecasting Workshop, Saturday, led by @hariharansuhas @michalbravansky ▶️ (Workshop) EGGROLL-IPO: Pluralistic Alignment via Decentralised Post-Training with Population Preferences, Pluralistic Alignment Workshop, Saturday, led by @alfie_lamerton ▶️ (Workshop Spotlight) Abstraction for Offline Goal-Conditioned Reinforcement Learning, DEMO Workshop, Saturday, led by @ClarisseWibault

It has been an absolute privilege and pleasure to build up @UCL_DARK with @egrefen, @robertarail and @jparkerholder over the past eight years. Yesterday, the UK government announced not just one but two national academic fundamental AI research labs. I am extremely excited to announce that @UCL_DARK will be sunsetted and merge with @FLAIR_Ox, @whi_rl, @UCL_LASP and AIRL, to form the British Open-ended Learning and Discovery (BOLD) Lab — @BOLD_Lab_AI. This is a huge moment for academic AI research in the UK. Backed with £30m by @UKRI_News and @EPSRC, it provides a unique opportunity to attract leading international academic talent to the UK, and equip them with the computational resources to do groundbreaking exploratory AI research (more on the computational resources soon). It also creates a mentorship network of academics, industry leaders and entrepreneurs to educate young talent on how to translate fundamental AI research into real world impact. I want to thank all the students who made @UCL_DARK successful, in particular our PhD alumni @MinqiJiang, @_samvelyan, @zhengyaojiang, @_robertkirk, @akbirkhan, @LauraRuis, @YingchenX, @PaglieriDavide, and the work of our honorary faculty @egrefen, @robertarail and @jparkerholder who were generously contributing to mentorship and research in their free time.

Let’s gooo 🎉🦋🚀