Carbon-X is the first model achieving frontier intelligence on DNA understanding! It is fluent in both English and DNA so you can chat to DNA! Biology and DNA understanding will have a huge impact and be the main focus of the frontier labs. Model:

Agent Manager is finally out! Orchestrate dozens of agents across harnesses (Claude Code, Codex, OpenCode etc.) effortlessly. Agents communicate and self-organize to help you achieve complex tasks and always keep running on HF spaces. Try it:

Today, we're releasing the Carbon Annotation Database and the model behind it, Carbon-A. We've used this model to discover 566.34 million new gene candidates across 22,617 species, thousands of which have never been studied like this before. We wet-lab validated a number of these new genes in well-studied species, such as in cats, chicken, and arabidopsis (plant), with our partners at Active Site and @UCSD, with more extensive wet lab results on previously-unstudied genomes coming soon.

We're releasing Carbon-A: an open model to discover new genes in DNA. With Carbon-A, we annotated genomes from 22k+ species and produced 566 million gene candidates, about 16× the number of gene annotations in the RefSeq dataset. We're releasing the candidates as a database, alongside the model. Initial wet lab experiments support previously unannotated gene candidates even in well-studied organisms. We think 2027 will be the year for biology. As AI labs move from math and coding into biology, we want scientists to have capable open models and resources they can build on without depending on the priorities and policies of a handful of closed labs. Today, we can sequence genomes faster than we can identify the genes they contain. Finding genes often requires experimental data or comparisons with well-studied species, leaving much of the tree of life poorly understood. Carbon-A predicts where genes are directly from DNA, identifying regions that contain instructions for making proteins. Even familiar genomes still have gaps. Across cats, Syrian hamsters, chickens, and the plant Arabidopsis, independent RNA experiments in the wet lab supported 239 gene candidates missing from RefSeq, a major reference annotation database. Our ambition is to make finding genes across life as accessible as AlphaFold made predicting protein structures.

We turned Claude Code, Codex, Hermes, Pi, @opencode and other coding harnesses into RL environments. No changes to the harnesses, no changes to the training code. Any open model, any task set, fully open source my friends! Same model, same weights: 62% under Mini-SWE-Agent, 33% under Claude Code. But training inside a real harness normally means reimplementing it as an environment, so most models get trained in a scaffold nobody actually ships. The fix is a proxy, not a rewrite. The harness thinks it's talking to a model API. It's actually talking to a capture proxy that speaks the 4 formats coding agents use (OpenAI Chat Completions, OpenAI Responses, Anthropic Messages, Gemini), forwards to @vllm_project, records the exact token IDs and logprobs vLLM sampled, and hands TRL sequences it can train on. The harness becomes the environment. 10 harnesses run through it today, none modified. And because you control the reward, you can shape behavior the harness never asked for. We added a small bonus for solving a task in fewer tool calls: on tasks it already solved, the model now uses 31% fewer calls, in every harness, and about half under Codex. Tested on LFM2.5-2.6B from @liquidai: → Train in one harness: better mostly in that harness (OpenCode 34% → 58%). → Train in 4 at once: better in all 4 (42% → 54%). → SFT on 3,189 rollouts from Qwen3.8-27B instead: plateaus at 47.5%, below both RL runs. Everything is open and reproducible: the capture proxy in OpenEnv, the trainer in TRL, the tasks, the SFT data, the training code and all 7 trained models. Bigger models and bigger runs next. Full guide:

🚀 Excited to announce that we are teaming up with @os4science to fund the software stack behind open source science! 🚀 Open science models do not run without open source software, but they rarely get the funding or credit they deserve. We're trying to change that 🤗🤗🤗

was looking for a quiet weekend but xiaomi dropped their rl envs repo last night to put in perspective, if you have to buy some tasks like this its usually hundred to thousand dollars per task so this repo is literally worth millions

Multi-harness RL training is coming to OpenEnv > pick a model. pick a harness. pick a sandbox. train > Claude Code. Codex. Gemini CLI. OpenCode. Pi. Kimi. OpenHands. and many more. > Async RL loop. fully open-source. end-to-end, harbor compatible dropping soon 👀

In the comments on my thread, several people pointed out that (1) You are still better off with a "straight line" AI solution. (2) Mathematicians remain free to wander around the path if they like (possibly with help from that same AI that found the proof.) This is true, but there are reasons why it can’t happen today: - AI models are heavily trained to nuke hard problems, not to marvel at the beautiful flowers found on the way to the bombing site - As long as AI companies collect solutions to hard problems like hunting trophies, they have little incentive to make that exploration a priority - For human mathematicians, the current research ecosystem does not reward exploring the territory around a known solution But none of this is inevitable, and I hope that collaboration between mathematicians and open-source AI builders can help make that "post-solution" exploration process happen.

It's frustrating that the discussion about safety and pacing of AI progress is once again led by a handful of people when the implications concern everybody. Let me lay out a constructive, alternative proposal. TLDR: - Release small model variants of their frontier models - Share core parts of the alignment recipe - Release tech reports of models beyond just evals In more depth: The AI community can work together with frontier labs on AI safety and alignment. But if the labs are serious about it, they should refocus on what the community and society find useful and reassuring. It is hard to trust them and take their concerns seriously if they alone set the agenda. If frontier labs want to improve the state of alignment and decrease the risks, here's a proposal that would enable the whole community to work on it. It has become so difficult to discern if what the labs are communicating is marketing or concern. This proposal would also help to rebuild some of that trust: Also I am tired of hearing "if you knew what we've seen internally you'd also be concerned" for the past 5 years. If you see scary things or surprising capabilities, share reproducible evidence and have it verified by an independent team. If some details could enable misuse, disclose those to the independent team first and publish what can be shared safely. So are these three points important? **Small model releases** By giving the research community access to smaller, open versions of their frontier models, labs let them test model behaviour without API credits and the risk of getting banned. Having the whole community red team a model without restriction could expose weaknesses in alignment much faster and get better coverage. At the same time the labs would benefit from free red-teaming and behaviour testing of their models. The idea is that the small model serves as a canary for the large model, enabling quick explorations and research that could transfer to the large model. Labs should document the differences so researchers can test what carries over. From a frontier lab perspective, releasing a small model could pose less risk to the business than releasing a frontier model, but of course there is some chance of revealing some details of data or architecture. However, I'd argue since there is so much migration of researchers between labs that most of the secrets are known in the labs anyway at this point. I'd like labs to be specific about which details they can't share and why. **Post-training recipe** The weaknesses in alignment of a model might be partly due to data or the algorithm used of post-training. Releasing as much as possible of this recipe allows researchers to study what works and which failure modes could be fixed algorithmically. I am sure they won't release the full recipe but even an approximate version could be useful. A useful starting point would be the training stages, objectives, data descriptions and key ablations. **Tech reports** The tech reports of the early days (e.g. GPT-2 or GPT-3) have led to some transparency what the frontier was up to. Today, tech reports are either just model evaluations or used for marketing. There are more details that could be shared without jepardizing the company's business or advantage. Again, a lot of the details are anyway known between the labs. Start with the methods, evaluation setup and failure cases. Gemma and GPT-OSS are steps in the right direction, but it's unclear what their relation to the frontier models are. We need more such releases and more transparency around them. Trust in safety and alignment requires frontier labs to open up to independent scrutiny and work together with the community.

Mathematicians are angry at AI solving their problems, and it's easy to dismiss this as gatekeeping. "A proof is a proof. They just want to keep their jobs / prestige / fun !" But I do think mathematicians have a point, and I'll try to explain using old Civilization-style maps