Ryan-Rhys Griffiths

My PhD thesis on applications of C̶h̶a̶t̶G̶P̶T̶ #GaussianProcesses in the natural sciences is now available online! arxiv.org/abs/2303.14291 A belated thanks to my examiners! @andrewwhite01 and @pl219_Cambridge #AI4Science #GaussianProcesses #BayesianOptimization

@Zek_Guo Thanks very much Zekun! Hope it's useful!

@Ryan__Rhys Huge thanks for sharing! I found you through GitHub and just come to X to follow you.

From J-1 to Green Card in 15 months. I'm open-sourcing my complete 1600-page EB-1A petition (pictured). Includes: → Complete LaTeX petition template → Example reference letters → Timeline In light of Trump's comments on streamlining the EB-1A process, there's never been a more important time to consider it. Huge thanks to @RazMarinescu , whose open-source EB-1A effort inspired me to take this path. ⚠️ I'm not a lawyer—consult one before filing. For education purposes only. GitHub: github.com/Ryan-Rhys/EB1A Blog post: ryan-rhys.github.io/ryan__rhys/blo… #EB1A #GreenCard

2023: It's hard to devise an LLM that solves a math problem. 2025: It's hard to devise a math problem that stumps and LLM. (...this in the context of competitive math questions, but we'll also get to research-level math soon)

Can you solve this Olympiad-level problem? This is the mind of problems LLMs have to solve to compete at the third AI Math Olympiad.

Most likely the most biggest and advanced reasoning competition in world right now.

@mgdurrant @AnthropicAI Congrats Matt!

Thrilled to share that I have joined @AnthropicAI as a life science researcher! I am confident that Claude will do amazing things to accelerate biology. Big things ahead!

OpenAI have just released an open-weight LLM. openai.com/index/introduc… This is great news for the AIMO3 competition we'll launch. Get your GPUs ready to fine-tune the lemmas out of GPT-OSS! :)

Below is a chronological thread that summarizes the main developments around the IMO25 and the controversial AI evaluation. (Note that due to managing the AIMO, I can't really comment on ongoing developments, and the links below should not be construed as me endorsing them.) 1. IMO announces that they cannot provide certification of the AI submission models, or guarantee scientific reproducibility. (imo2025.au/news/the-66th-…) 2.OpenAI publishes their announcement on X that they won gold. (x.com/alexwei_/statu…) 3. This raised various questions about the circumstances in which this evaluation was carried out, and whether the fact that the announcement came before the IMO ceremony wasn't bad timing. (x.com/demishassabis/…) 4. At that same time, DeepMind announces the they, too, achieved gold. (deepmind.google/discover/blog/…) [A number of further companies and organizations I have heard will also make announcements, so stay tuned.] 5. This was then finally picked up by Hacker News, Reddit and other platforms. (news.ycombinator.com/item?id=446371…) 6. At the same time, prominent voices from the wider community expressed dissatisfaction of this new way of doing science. (linkedin.com/posts/gerko_ne…) 7. Finally, even mainstream news picked it up, e.g. NY Times, albeit with a slightly inaccurate message, as the correctness of the final solution was significantly more strongly emphasized, than the process by which one arrived at that solution. (nytimes.com/2025/07/21/tec…)

The IMO is changing - and is walking in the footsteps of chess competitions. Last year it was just the #aimoprize that it hosted. This year there was an associated event where a handful of AI companies and organizations tested some of their models on the IMO problems. The story with math is similar how the story with chess will go, where chess engines these days routinely beat humans, and so there are human-vs-human competition tracks, machine-vs-machine competition tracks, as well as machine_assisted_human-vs-machine_assisted_human track. The latter is called "Advanced chess". en.m.wikipedia.org/wiki/Advanced_…. I predict in a few years there will be an "advanced IMO."

IMO2025 has begun. Last year, AlphaProof won a silver medal (though no paper nor software was released so we have to trust that claim and the mathematicians that had access). This year, a whole bunch of organizations requested access to the IMO problems, so it will be interesting to see how they perform. My bet is that we'll see a gold medal for the "formal Olympiad". While impressive, that still will not be an accurate assessment of these systems, as 6 problems don't make a convincing benchmark (though they work wonders for marketing). As an example, AlphaGeometry and my own improvement of it, Newclid, also solve Olympiad-level geometry problems, but cannot prove the Pythagorean theorem (Newclid has some limited support for it though). This slipped under the radar in the first release of AlphaGeometry, since benchmarks weren't large enough. Small benchmarks are dangerous.

Today we're releasing ether0, a large reasoning model for chemistry trained with reinforcement learning via GRPO. Read more in our exclusive with Nature below: Nature: nature.com/articles/d4158… Preprint: storage.googleapis.com/aviary-public/… Model: huggingface.co/futurehouse/et… Benchmark: huggingface.co/datasets/futur…

Today, we’re announcing the first major discovery made by our AI Scientist with the lab in the loop: a promising new treatment for dry AMD, a major cause of blindness. Our agents generated the hypotheses, designed the experiments, analyzed the data, iterated, even made figures for the paper. The resulting manuscript is a first-of-a-kind in the natural sciences, in which everything that needed to be done to write the paper was done by AI agents, apart from actually conducting the physical experiments in the lab and writing the final manuscript. We are also introducing Robin, the first multi-agent system that fully automates the in-silico components of scientific discovery, which made this discovery. This is the first time that we are aware of that hypothesis generation, experimentation, and data analysis have been joined up in closed loop, and is the beginning of a massive acceleration in the pace of scientific discovery that will be driven by these agents. We will be open-sourcing the code and data next week. Robin is a multi-agent system that uses Crow, Falcon, and Finch, the agents on our platform, to generate novel hypotheses, plan experiments, and analyze data. We asked Robin to find a new treatment for dry age-related macular degeneration. Robin considered the disease mechanisms associated with dry AMD, proposed a specific experimental assay that could be used to evaluate hypotheses in the wet lab, and proposed specific molecules we could test in that assay. We tested the molecules and gave it the resulting data, which it analyzed before proposing more experiments. In the end, it identified Ripasudil, a Rho Kinase inhibitor (ROCK inhibitor) that is approved in Japan for several other diseases, which seems very promising as potential treatment for dry AMD. It also identified specific molecular mechanisms that might underlie the effects of Ripasudil in RPE cells, from an RNA sequencing experiment it proposed. To be clear, no one has proposed using ROCK inhibitors to treat dry AMD in the literature before, as far as we can find, and I think it would have been very difficult for us to come up with this hypothesis without the agents. We have also run the proposed treatment by several experts in AMD, who confirm that it is interesting and novel. Moreover, this project was fast: with Robin in hand, the entire project took about 10 weeks, which is way shorter than it would have taken if we had been doing all of the in-silico components ourselves. Important caveats: We are real biologists at FutureHouse, so I want to be clear that although the discovery here is exciting, we are not claiming that we have cured dry AMD. Fully validating this hypothesis as a treatment for dry AMD will take human trials, which will take much longer. Also, this discovery is cool, but it is not yet a "move 37"-style discovery. At the current rate of progress, I'm sure we will get to that level soon. Congratulations to the team. Congratulations in particular to Robin, which generated the hypotheses, proposed the experiments, analyzed the data and generated the figures. And major congratulations also to the human team, which built Robin: @MichaelaThinks, @agreeb66, @benjamin0chang, @ludomitch, Mo Razzak, Kiki Szostkiewicz, and Angela Yiu.

@rlacombe @FutureHouseSF @andrewwhite01 @SGRodriques Thank you @rlacombe !

Congrats to the whole @FutureHouseSF team on the launch on their Platform today! 🚀🚀🚀 Well done @andrewwhite01 @SGRodriques @Ryan__Rhys and team!

The FutureHouse platform is now live and ready to use, featuring a variety of ways to leverage language agents in scientific endeavors: platform.futurehouse.org

The next frontier for AI Agents in Science will be data analysis. Today, we're releasing BixBench, the most sophisticated benchmark yet for data analysis in biology. Agents that can do these tasks will be powerful tools for discovery. So far, they're not even close.

Half of an AI scientist is rejecting or accepting hypotheses. @FutureHouseSF and @SciMac just put out ~300 novel hypotheses from ~50 published papers along with ground-truth data. Humans take 4.2 hours to solve these and frontier models get 10-20% correct. SWE-bench for bio

@dorsa_rohani @arian_jamasb @samuel_stanton_ @austinjtripp

Who are the best people to follow on X for deep chemical & bio tech?

@RhysGoodall @andrewgwils No idea! Let me know if you find something!

Is there any good literature on the use of deep set models as part of deep kernel learning GPs for bayesopt? @Ryan__Rhys @andrewgwils

(3/3) I had the great pleasure of seeing old friends, making new ones and being stopped by some of my followers. In particular: @Clement_Bonet_ @spectraldani @austinjtripp @BAristimunha @tkipf @c_voelcker @lorenzgiusti @jessdafflon @Ryan__Rhys @Joshua_Bambrick @ire_cannistraci @naturecomputes @_Jaivardhan_ @avt_im and many more!

@AcerFur Very interesting! Thanks for sharing!

@Ryan__Rhys An incredibly difficult maths benchmark just dropped- perhaps you'd be interested in it! arxiv.org/abs/2411.04872

What are the mathematical capabilities of #LLMs and how are they evolving with time? GHOSTS is a new advanced mathematics benchmark for large language models accepted to #NeurIPS2023 CITATIONS: If you spot something you feel we should cite, leave a comment before the camera-ready deadline! Paper: arxiv.org/abs/2301.13867 Leaderboard: ghosts.xyfrieder.xyz