From better modelsto tighter scientific loops

What happened: Orakl Oncology raised an oversubscribed $34M Series A co-led by Norrsken VC and Singular, taking its total to $50M. New investors are InfraVia Growth, Serena, Bpifrance's Patient Autonome Fund, teampact.ventures and Plug and Play, with Verve Ventures, Speedinvest and HCVC following on and angels including Thomas Wolf. Co-founders Fanny Jaulin (CEO), Gustave Ronteix (CTO) and Diane-Laure Pagès (COO) grow patients' tumours as organoids, screen drugs on them and link each result to the patient's records.

Why it matters: About three quarters of cancer candidates fail in Phase 2, the release says, and Orakl sells drugmakers a reading of which drugs, and which patients, will respond before that stage. The money is for new models, five times the data, lung and gastric cancer added to colorectal and pancreatic, US and UK teams for more than 10 drug programmes, and finding new co-targets. Philip Tagari, chief scientific officer of insitro, who helped bring Amgen's KRAS inhibitor Lumakras to patients, joins as an adviser.

Evidence and limits: The 90% accuracy in predicting trial outcomes and the 18 of the 20 largest drugmakers in its pipeline are the company's own figures; the release names no paper or trial set behind them. The round was read in participant InfraVia's release, which gives no valuation.

What happened: Chuangdongfang Investment, the thermal-materials maker Feirongda and Jinhui Big Data took part; no lead was named. Chairman Nanjiang Chen, a Beihang University PhD who worked at its School of Mechanical Engineering, founded it in 2021. It sells modular chemistry workstations (NexoLab), software that runs instruments as agents (AI-LabOS) and models trained on its lab data (MoleMind). The money is for R&D, new industries and the cloud lab. Customers it names include the Chinese Academy of Sciences, Boehringer Ingelheim, Hengrui and Gotion.

Why it matters: Chen's case is that public chemistry data leaves out full reaction conditions and failed runs, so the company built its own robot lab this year to supply data for its models, and has begun trying out experiments as a service from it, with customers watching the data as it comes in. Feirongda, which makes thermal-management materials and liquid cooling, says AI computing infrastructure is raising the bar for cooling and thermal materials, and it wants automated testing for materials formulation: a chip-cooling supplier paying for automated materials R&D.

Evidence and limits: The tens-fold gain in experiment speed and the good repeat-order rate are the company's own words, with no figure or study behind them. The amount is the round figure of 100M yuan, no valuation was given, and the reports name no lead. Chen says AI-designed experiments still need a chemist's sign-off.

What happened: Google DeepMind released AlphaGenome 2, which reads a million letters of DNA with one chosen tissue, cell type or state and predicts expression, chromatin and, newly, DNA methylation there. Training included over 107 million single-cell profiles. On immune-cell variants from the TenK10K cohort, which it was not trained on, it found 30% of likely causal variants at 90% precision, against 16% for the first model fine-tuned on single cells. In CRISPRi experiments it matched distant DNA switches to their genes better than AlphaGenome 1 (0.78 vs 0.73, out of 1).

Why it matters: Most disease-linked variants sit in non-coding DNA and act only in some cells; telling which cell is the step a drug programme needs to go from a genetic association to a gene. The model reproduced a 2024 finding that a distant variant drives IBD by boosting ETS2 in gut immune cells, and James Lee of the Francis Crick Institute, who led that study, told Nature it got in 30 minutes what took his team a year and a half of experiments. Peter Donnelly, chief executive of Genomics, says it looks forward to adding the model to its Mystra platform.

Evidence and limits: Every figure is DeepMind's own, from a paper on its own site with no DOI and no peer review. The paper says that at high precision it still misses most likely causal variants, and does worse outside open DNA regions. Peter Koo of Cold Spring Harbor Laboratory told Nature the ideas are not novel but the engineering and performance are impressive; Lee says it does not yet remove the need for experiments. It is not validated for clinical use.

By Adam Gayoso, Pushmeet Kohli, Žiga Avsec

What happened: Miguel Marques and Silvana Botti's group connected Claude Code, running unattended, to Alexandria (about 15 million DFT calculations), its structure generator, potential and property models, and a graph of 17.4 billion reactions between 193,416 stable compounds. Asked for an untried solar absorber, the agent dropped a candidate whose best reaction releases only 6 meV/atom and proposed Sr2ZnP2 with crucible, atmosphere and heating steps. In the Ba-Zr-S system it found an unreported Ba4ZrS6. With web search alone, it proposed a compound the literature already discusses.

Why it matters: Most materials agents so far give a model ways to run calculations, which a frontier coding agent can already do itself. Here, in one run per setup, the difference tracked a group's existing data and a way to reason about synthesis: with the database and models but no reaction graph, the agent still found candidates but took its recipes from the literature. For computational groups that suggests a role serving data and models to agents that experimental groups query in plain words, with tool descriptions that say when each number is wrong.

Evidence and limits: Every candidate is computed and none was made. Each example is one run, which the authors call examples, not statistics. A later hybrid-functional calculation gave the agent's air-stable pick, Sr2ZrCu2S5, a 0.98 eV gap, too small for a solar absorber; the backup polymorph it offered came out at 1.63 eV. The group tested its own tools.

By Paulo R. Pires, Silvana Botti, Miguel A. L. Marques

What happened: Jian-Xun Wang's group gave GPT-5-mini agents a GPU flow solver that returns gradients through chaotic simulations. The agents propose a closure, a formula or small network standing in for turbulence too fine for the grid, build its training program and choose whether to run, probe or train it. In channel flow, scored against a fine reference simulation, the agents' best model of the drag at the wall had an error of 16.5 against 55.9 for the standard equilibrium model. Each run had 30 experiments.

Why it matters: Turbulence models are still mostly written by hand, and engineering and natural-flow simulations depend on them. Earlier LLM searches for such models scored each proposal by running it; here the agent can also fit a proposal's parameters by gradient. With the formula fixed, that recovered the reference coefficients, which forward runs alone did not reach in the same budget, and the gradients the solver reported changed the agent's own choice of settings. The language model picks the form; the differentiable solver is what makes it trainable.

Evidence and limits: The authors call the agents' wall models comparable to the standard one, which still matches the reference velocity profile better in the log region. All three are textbook flows on coarse grids, each model fitted to one statistic; the channel case was one 82-hour run. The models largely rebuild known forms, and the spectral score could not tell structurally different subgrid models apart. A preprint, testing the group's own system.

By Yi Liu, Xiantao Fan, Mingqi Xu, Jian-Xun Wang

What happened: Google rolled Gemini 4 Carbon into Google Antigravity for Google AI Ultra subscribers, and scientists and researchers can apply for a chance at six months of free access to both Carbon and Argon at its top usage tier; enterprises get Carbon through Gemini for business. Alongside it, /swarm (preview) splits one prompt among a coordinator, parallel workers in separate git worktrees and adversarial verifiers that run tests and challenge each change, all posting to a shared board. Each agent can be pinned to a different model.

Why it matters: When Google announced Carbon on 28 October it went to cyber defenders first, with no date for anyone else; two weeks later scientists can apply for free access to it. Carbon is the model whose agents wrote the proofs in DeepMind's Signal verification, published the same day, and Antigravity is where DeepMind has shown its own agents hunting antimicrobial peptides and redesigning a drug. /swarm turns many agents with separate checkers into a preview feature in the Antigravity app and command line.

Evidence and limits: This is Google's own product post. The CursorBench 4.0 and WANDR rankings it cites come with no figures there, and /swarm's case rests on five weeks of Google's internal use, with no numbers. Google warns that swarms use up quota much faster than a single chat. Free access for scientists is by application, with no number of places given in the post.

What happened: Google DeepMind's AlphaProof Code, agents that wrote proofs on Gemini 4 Carbon, proved forward secrecy, authentication, post-compromise security and handshake secrecy for libsignal, Signal's reference implementation, in its compiled ARM machine code. The trusted part people must check is about 26,000 lines of specification and machine semantics; the agents wrote about 6.5 million lines of proof, each checked by Lean. A team of five did it in four weeks. With two old, fixed bugs put back, the system proved both builds insecure.

Why it matters: Formal proofs of real software have cost years of expert work: the seL4 kernel took twenty person-years, and it was written to be verified. If agents write the proofs and people check only what is being proved, the cost moves to writing the specification, which changes who can afford verified code. DeepMind says the agents sent only 1% of the code back for a person to look at again; DeepMind names medical-device firmware and power-grid software. The proofs are in Lean, the language mathematicians use to check proofs by machine, and DeepMind has opened them.

Evidence and limits: This is DeepMind's own paper, not peer reviewed. The symbolic proof treats the cryptography as perfect, with two honest parties, bounded inputs and no side channels; the computational one rests on the primitives' hardness and does not cover quantum attackers. Both cover DeepMind's own command-line build, not Signal's apps, and rest on 26,000 trusted lines and on Lean. Signal's part is its CTO's endorsement and feedback on the method, not an independent audit.

By Swarat Chaudhuri, Pushmeet Kohli

What happened: Proximal Ventures will write pre-seed and seed cheques of $500,000 to $1.5 million and has already backed seven start-ups, TechCrunch reported on 10 November. Its backers include the venture firms SignalFire and Kleiner Perkins, Bertelsmann Healthcare Investments, Nan Li and Zavain Dar, co-founders of Dimension, and Amy Abernethy, a former principal deputy commissioner of the FDA. Raoof spent two years as a principal at SignalFire and earlier led business development at Iterative Health and partnerships at Flatiron Health.

Why it matters: The fund bets on the service side of drug development. Drugmakers have long paid contract firms to make thousands of compounds, work out how to manufacture a drug at scale and analyse trial data, and Raoof argues AI can deliver that same work cheaper and faster, as it has begun to in legal and financial services. So the buyer he is counting on is pharma's outsourcing budget, which pays for services, rather than software, which pharma has not been thought a good buyer of.

Evidence and limits: Fund size, cheque size and strategy are as TechCrunch reported them, with Raoof quoted; no announcement or filing from the fund was read. Of the seven start-ups only ChronicleBio is named, and the report gives no customer or result for any of them.

What happened: Kenneth Stanley, who leads the team, published an essay naming four things an open-ended discovery system needs: generating ideas, judging which are interesting, an internal representation suited to insight, and managing many research lines. Four arXiv preprints with Joel Lehman, Alon Albalak, David B. D'Ambrosio and Thomas X. Sheppard cover two of them. In one, small models made to forget a topic propose more novel ideas; in another, a training add-on cuts the steps a network needs to learn a modular arithmetic task over twelvefold in one setting.

Why it matters: Lila describes its goal as scientific superintelligence, and its automated lab is meant to be fed by machine-generated hypotheses. These papers show what the team Stanley leads at Lila is working on for that hypothesis side. They argue that scaling models up will not by itself make them creative, which runs against the common view that scaling will fix it.

Evidence and limits: Method papers on language models and small networks: 1-billion and 7-billion-parameter models, an image task and modular arithmetic. None involves a laboratory experiment or a scientific result, and none is peer reviewed.

By Kenneth O. Stanley, Joel Lehman, Alon Albalak, David B. D'Ambrosio

What happened: Researchers at IBM Japan, Mitsubishi Chemical, Keio and NVIDIA trained transformers to write circuits for sample-based quantum diagonalization, on a simulator, then on IBM's Heron processor ibm_kobe at 46 and 70 qubits. Applied without retraining to methyltin trihydroxide and its hydrates, minimal models of tin photoresists for extreme-ultraviolet lithography, 27 of 28 runs at 46 to 70 qubits came within 15 mHa of a classical DMRG reference, median 4.5. At 56 to 70 qubits the chosen circuits used 34 to 72 times fewer two-qubit gates than an LUCJ circuit.

Why it matters: Tin compounds are a leading candidate for the resists that EUV lithography patterns chips with, and how their tin-carbon bond breaks under light needs multireference chemistry, the strongly correlated kind quantum computers are meant to help with. A chemicals maker co-authoring hardware runs on these molecules shows where industrial quantum chemistry is pointed. The method's point is that the processor itself gives the training signal at sizes where exact classical simulation is out of reach, from about 46 qubits.

Evidence and limits: The reference is a classical DMRG calculation, so the runs match classical answers rather than beat them, and no resist was made or measured. Each circuit was sampled once and each setting trained with one seed; the authors call the evidence of transfer to new chemistry qualitative. The gate savings are for the best of ten circuits, at about 11 times the processor time per job.

By Kunal Kumar, Qi Gao

What happened: Tushen Zhihe, founded in Wuxi in late 2023 by Yuguang Wang, an associate professor at Shanghai Jiao Tong University's Institute of Natural Sciences, closed two rounds totalling several hundred million yuan. Shanghai Kechuang Group led the Pre-A, joined by Zhongsheng Yinling, Zhoushan Tuyi and others; Jinding Capital, MegaRobo, Zero2IPO Holdings and others invested in the Pre-A+. The money is to take several of its own programmes toward IND filings, upgrade its ToursynClaw design platform and hire. Its previous round, in September 2025, was an angel+ of around ten million yuan.

Why it matters: The money goes first to the company's own drug candidates, and its own candidates are where an AI protein-design company has to show that its designs work as medicines. MegaRobo, a maker of lab automation, invested to link its automated labs to the company's design-and-test loop, and the Pre-A came from Shanghai's state-backed venture group, which calls AI for science in biomedicine one of its main investment directions.

Evidence and limits: The results are the company's own, in the round's announcement: more than 100 antibody-engineering projects checked in real experiments, one design-and-test round in 14 days, more than 5,000 experimental data points. No paper or partner-reported result was found, its programmes and targets are not named, and neither the split between the two rounds nor a valuation is disclosed.

What happened: Google announced TPU 8i Max, which puts two TPU 8i chips on one package to double compute, on-chip memory and memory bandwidth in the same rack space, and TPU 8tx, which pairs TPU 8t silicon with AMD EPYC hosts so data and model weights stay protected and isolated even while in use. With the training TPU 8t and inference TPU 8i from April, that makes four TPU systems in a year. Amin Vahdat, Google's chief technologist for AI and infrastructure, says TPUs now have nearly 100 customers, universities among them.

Why it matters: Neither system is a new chip: 8i Max packages two existing 8i chips together, and 8tx reuses 8t silicon. Google says that is the point, since a new system takes months and a new chip years, so new configurations can follow what customers ask for. Two inputs change. What can be bought: four systems in a year, one for fine-tuning on private data that stays protected while the chip works on it. Chip design: Google says AI now works on its own hardware, AlphaChip in TPU design and Gemini producing layouts in hours rather than weeks.

Evidence and limits: Every figure is Google's own, measured against its other TPUs, with no independent benchmark. The post gives no absolute specs, prices or availability dates for the two new systems, names no science customer, and shows no example of an AI-made layout.