Timeline

What happened: 10x Genomics, which makes the Chromium single-cell sequencing instruments, launched Sentira Single Cell on its 10x Cloud. A researcher asks a question, or lets the agent propose one from the data; the agent drafts an analysis plan for the researcher to review, writes and runs the code, and keeps the methods and code in an editable notebook. It runs on Anthropic's Claude models. It is paid in credits, and 10x told GEN a complete analysis takes about 60.

Why it matters: Single-cell work usually stalls at analysis: each lab's question needs its own code, and many biologists who produce the data cannot write it. Putting an agent inside the instrument maker's own cloud is meant to let Chromium users take that step without a bioinformatician. Michael Schnall-Levin, 10x's chief technology officer, told GEN that analyses that took weeks could take hours. It extends the Sentira platform 10x announced at the end of September, whose spatial version is in early access.

Evidence and limits: The release shows no test of the agent's analyses against a bioinformatician's, and 'weeks to hours' is the company's estimate. Schnall-Levin told GEN the agent is not perfect and its output needs review before it goes into a paper.

What happened: Stepan N. Ostarkov, Michael G. Medvedev and colleagues at the Zelinsky Institute in Moscow gave models 60 labelled molecules per task. Prompted with the examples, GPT-6 Astra beat the best chemistry model on solubility, hydration energy, lipophilicity and two quantum-chemistry targets: R² 0.85 against 0.53 on orbital energy, 0.97 against 0.63 on hydration. Chemistry models won only on BACE1 enzyme inhibition, the control. On orbital energy the best of them needed 1,000 molecules to pass Astra's score on 60.

Why it matters: Most chemistry projects have fewer than a hundred measurements, which is exactly the case chemistry foundation models were built for. Here a general model prompted with the data did better on the property-prediction tasks tested, outside the drug-like classification the chemistry models were trained around. If it holds, a lab with a small dataset should try a frontier model before fine-tuning a chemistry one.

Evidence and limits: All six benchmarks are public, so the general models may have seen the answers in training. With labels shuffled they still scored well, and the authors flag likely memorisation for Claude Opus 5.5. Each task used 100 molecules over 15 random splits; this is a preprint.

By Stepan N. Ostarkov, Gregorii A. Semakin, Ivan A. Bespalov, Michael G. Medvedev

What happened: Timur Bazhirov at Exabyte (Mat3ra), with co-authors at Berkeley Lab, Stanford, SLAC, NVIDIA, UC Berkeley and other institutions, released Q3, a density functional theory engine built from scratch. Claude Code, Gemini and Cursor agents wrote its 82,200 lines of C++ and over 1,800 tests in weeks; the scientists set the interfaces, equations and tolerances. On silicon and copper test cells its total energies agree with Quantum ESPRESSO's within 0.014 mRy. It is open source under Apache 2.0.

Why it matters: DFT calculations are the main source of training data for machine-learned materials models, and the codes that run them are decades-old Fortran that is slow to extend. If agents can write and test a new implementation in weeks while scientists check the physics, a new method can reach a fast, validated code much sooner. Q3 already runs on GPUs at NERSC and writes its results in BerkeleyGW's file formats for many-body calculations.

Evidence and limits: Accuracy and speed are checked against one reference code on small cells of up to 54 atoms that the authors chose, and outside review so far is black-box checks of its numbers. The paper gives no count of human hours, so 'weeks' cannot be checked.

By Vsevolod Biryukov, Timur Bazhirov

What happened: Flagship Pioneering combined the platforms of three of its companies, which BioPharma Dive says had raised $374M between them, with its in-house AI unit, Pioneering Intelligence. Adrian Rawcliffe, formerly CEO of Adaptimmune, is CEO; Flagship senior partner Alec Reynolds chairs the board, and Noubar Afeyan is a co-founder. It starts with programmes in blood disorders such as myelofibrosis, in metabolism, including partnered MASH work, and in immunology. Cellarity's sickle cell drug CLY-124 goes to a separate company.

Why it matters: Each company held one kind of evidence: Cellarity read the state of whole cells, Etiome followed how a disease changes from stage to stage, and Empress showed what a molecule actually binds. Flagship is betting that the three together, plus data on how cells respond when a gene is silenced or a chemical is added, can tell which targets cause a disease, and so which drugs will work in patients before a trial. Reynolds framed the target as the roughly half of drugs that fail in Phase 2.

Evidence and limits: No new funding was announced. The 150-million-cell foundation model, which the release calls the industry's largest, and the more than 30 prediction models are the company's description; no result from them is shown.

What happened: Physicists at National Taiwan University, Shanghai Jiao Tong University, SLAC, the University of Washington and others pretrained a model of whole collision events on 500 million simulated events, then tuned it for four analyses. In simulated searches for new heavy particles and exotic Higgs decays it beat XGBoost, TabPFN and SPANet, most clearly with little data, and in simulation it measured top-quark entanglement 37% more precisely than a published benchmark. In real 2016 CMS data it found the known Upsilon meson at 7.6 sigma, against 6.4 for the published method.

Why it matters: Collider physicists today train a separate model for each analysis. EVENET tests whether one pretrained model can serve many: one large pretraining run, on 512 GPUs at NERSC, then cheap fine-tuning for each new question. The gain is largest in searches for rare signals, where each new-particle hypothesis comes with only thousands of simulated signal events. The pretraining data and three of the four downstream datasets are public on Hugging Face under the MIT licence.

Evidence and limits: Only one test uses real collisions, and it finds a particle already known; the other three run on simulated events. The authors say the model does not yet carry detector and theory uncertainties through, and that use in a live LHC analysis is the decisive test.

By Ting-Hsiang Hsu, Vinicius Mikuni, Yuan-Tang Chou, Yulei Zhang

What happened: Fanxu Meng, Kedar Hippalgaonkar and colleagues at UC Berkeley's Singapore centre, Nanyang Technological University, the National University of Singapore, Monash University and Lawrence Berkeley National Laboratory built SKIER: AI models call analysis procedures the researchers wrote, and plain code does the calculations. On 49 records from the group's own published study of methanol oxidation on platinum, all 11 AI configurations finished the data processing with the shared procedures and none without. The models then proposed three mechanisms and tests to tell them apart; the team ran the tests.

Why it matters: AI models are increasingly asked to interpret experiments and propose mechanisms, and their claims are often hard to trace to the measurements behind them. SKIER splits the work: researchers fix how each measurement is analysed, the model chooses and reasons, and each conclusion points back to its data. In the case study the loop closed once: isotope-labelling, residence-time and oxygen-exchange tests argued against two of the three mechanisms and placed C-O bond cleavage before formic acid forms.

Evidence and limits: One reaction system, from the authors' own earlier study. The 100 single-choice questions on which GPT-5.6-sol scored 100% were expert-designed, and the paper does not say by whom. The authors note that intermediates below the detection limit and pathways they did not consider are not ruled out. A preprint.

By Fanxu Meng, Kedar Hippalgaonkar

What happened: Edison Scientific, the FutureHouse spin-out led by CEO Sam Rodriques, announced an agreement to deploy the Edison AI Platform and Kosmos across Pfizer's research and development. Kosmos will draw on Pfizer's data in oncology, internal medicine, immunology and other areas, and Edison's forward-deployed scientists and engineers will build it into specific workflows with Pfizer's research teams.

Why it matters: This is the third Kosmos deployment Edison has announced this year, after Incyte in May and Population Health Partners in June. Here the AI scientist reads a large drugmaker's internal records, including programmes that did not work, which published papers rarely report. Rodriques describes the aim as letting what each programme learned inform the others.

Evidence and limits: Only Edison announced it: no Pfizer release was found, and no Pfizer person is quoted. No money, duration or scope is stated. It is a deployment, not evidence that Kosmos improves Pfizer's decisions.

What happened: Mistral released the weights of Mistral Large 4 ("Le Chonk"), a 1-trillion-parameter model with 52 billion active, under a modified-MIT licence, free to commercialise except for model-as-a-service providers. It reports a tie with the top open-weight model GLM-5.3 on a coding-agent index (48%) at 47% fewer tokens, and a 96.6% refusal rate on malicious cyber requests, the highest it measures among open models. It also says that in live traffic it saw a potential for the model to help conduct real attacks, and is changing its guardrails.

Why it matters: Mistral's pitch is the open frontier: a model researchers and companies can download and run on their own machines, trained in Europe for what it calls sovereign AI. The open weights are the part that bears on open science tooling. The self-disclosed finding is the one to note: a lab releasing a capable model's weights while saying the same model could help attack live systems, and shipping anyway with guardrails it is still revising.

Evidence and limits: Every benchmark figure is Mistral's own, and it says a full technical report is still to come. The uplift finding is Mistral's own observation from its live traffic, with no number attached.

What happened: Opentrons Labworks, a New York maker of open benchtop lab robots, closed a $40M Series E led by the healthcare investor Avego, with BroadOak Capital Partners; SoftBank Vision Fund and Khosla Ventures are existing backers. It says it has placed over 11,000 robots in more than 80 countries. CEO James Atwood says the money goes to robotics, software and data tools, AI-run experiments and new markets; James Flexner, Avego's managing partner, says every AI-driven discovery still has to be tested in a real lab.

Why it matters: The company's case is that models now propose experiments faster than labs can run them, so the robot that does the pipetting is where the queue backs up. Opentrons has a large installed base and an open programming interface, which is why outside teams have already had Claude drive its Flex (Tetsuwan Scientific) and GPT-5.6 Terra drive its OT-2 (Duke's PEARL). The round is money betting that accessible, open robots like these become the usual way AI models reach a wet lab.

Evidence and limits: A financing, not a result. The robot count is the company's own figure, and the release gives no valuation, no total raised and no measure of how much of its robots' work is run by AI today.

What happened: Cascade will treat blood immune cells from 12 healthy donors with cytokines, chemokines and drugs, alone and in combination, and read them at several time points: 100 million single-cell RNA profiles from Parse's GigaLab and more than 11,000 profiles of over 1,000 secreted proteins from Nomic's Omni 1000, across more than 150,000 perturbation measurements. GSK, Pfizer, Sanofi, Graph Therapeutics and Turbine are founding partners. Data generation has started, and the set will be licensed.

Why it matters: Models of how cells respond to drugs, the virtual cell, are short of data that follow a response over time and measure more than RNA. Cascade measures the RNA inside each cell and the proteins the cells release, from the same samples and time points, so a model can be tested on whether cells' responses predict the proteins they secrete, or held out on unseen donors and perturbations. GSK, Pfizer and Sanofi signing on before any data are released is a sign of how much drugmakers want this kind of data.

Evidence and limits: An announcement: no data have been released yet, and the companies give no date, price or partner terms. The set is licensed, not open.

What happened: Lingxin Cheng, Jack Shireman, Christina Kendziorski and colleagues at the University of Wisconsin-Madison built SMART, about 20 Claude agents that read a published single-cell RNA study, list its findings, then write and run analyses testing each one in another dataset. Across 121 studies of six cancers it wrote 726 reports: 46% of the cross-cancer reports found a transfer, against 75-88% within one cancer. In 10 cases checked by hand, 92-97% of its outputs were correct, and fed summaries with every finding reversed, it rated all 15 low or middle-low.

Why it matters: A published finding is rarely tested outside the setting it came from, because checking it means rereading the paper and redoing the analysis. SMART makes that check routine and points to what is worth following up. ORACLE, a 23-gene lung cancer risk signature, is cited by 161 studies and none tested it in another cancer; after SMART flagged it, the authors found high scores linked to worse survival in public liver (hazard ratio 1.92), pancreatic (1.73) and mesothelioma (3.04) cohorts.

Evidence and limits: Every check reuses existing public data; no new experiment was run. ORACLE showed no significant link to survival in ovarian, brain or colorectal cancer. The team audited its own system, and the preprint is not peer reviewed.

By Lingxin Cheng, Jack Shireman, Christina Kendziorski

What happened: Conor Igoe, Siddharth Narayanan and colleagues at Edison Scientific and MIT CSAIL built 96,000 episodes from drug-response RNA data, CRISPR gene-dependency screens and GWAS results. A model reads a run of related results and states the probability that a held-out result supports a hypothesis. GPT-5.6-sol and Claude Opus 5 rank true above false (AUROC 0.737 and 0.752) but are underconfident, with Brier scores no better than a flat 50%. Qwen3-30B post-trained on scoring rules is called Ibis0; its classifier version reaches a Brier of 0.177 and AUROC 0.820.

Why it matters: An agent that chooses experiments needs to know how far a result should move its belief, not only which answer is likely. The benchmark measures that, and the training improves it. In a simulated task of picking the next experiment, Ibis0 paired with the paper's information-gain estimator got about 2.5 times the information from its first pick that the best prompted frontier model did; prompted alone it did no better. Code, data and models are released.

Evidence and limits: The episodes are structured summaries of published data, with yes-or-no hypotheses balanced at 50%, and experiment choice was tested against a simulator, not in a lab. The experiment-choice gain shows in two of three domains. One model family at one size was trained, on the company's own benchmark.

By Conor Igoe, Siddharth Narayanan

What happened: Ali Saffar Shamshirgar, Johanna Rosen and colleagues at Linköping University, Tallinn University of Technology and Aalto University trained a classifier to recognise the layered structure of MXenes, thin metal carbide and nitride sheets, and ran it over the Materials Project and OQMD. With database tags and literature checks, it turned up 33 compounds reported as made but never recognised as MXene candidates. The team then made seven MXenes with a self-sustaining combustion reaction that needs no furnace and finishes in minutes, and 11 new ones built on seven rare-earth metals.

Why it matters: Most MXenes are still made by etching with hazardous acids such as hydrofluoric acid, in slow multi-step processes. Here the model's job is not to invent materials but to find ones already in the literature under other names, and those finds pointed the team to rare-earth carbides with halogen surfaces, a region where almost no MXene had been made. The combustion route is a candidate for making them at scale.

Evidence and limits: The classifier only sorts structures already in the databases; whether each of the 33 really behaves as an MXene is not tested. The new phases are multilayer powders, not delaminated sheets, band gaps were measured, but the magnetic states come from calculations. One group's work.

By Ali Saffar Shamshirgar, Johanna Rosen

What happened: The New York company buys or licenses Phase 1 and Phase 2 drugs from other companies and runs their development itself. Its new Drug Development Accelerator Fund has $600M committed by research foundations, endowments, hospitals and philanthropies; Formation Bio is the general partner through a subsidiary and puts in money of its own. Each drug sits in its own company. CEO and co-founder Ben Liu, chief business officer David Steinberg and chief scientific officer Michael Ehlers spoke in the release.

Why it matters: It is another way for an AI company to pay for drug development: Formation Bio raises money against individual drugs, and investors buy into the drugs, not the platform. Liu said it opens paths to liquidity without selling the parent company and should lower its cost of capital over time. The claim to test is that one shared, AI-supported development team takes each drug to a clinical result more cheaply, within 2 to 4 years of buying it.

Evidence and limits: This is a fund for drug programmes, not an equity round in Formation Bio. No limited partner and no drug the fund will buy is named, and the release shows no case where the platform shortened a programme.

What happened: Google DeepMind announced Gemini 4 Carbon, which it calls its strongest model, announced by Koray Kavukcuoglu (SVP, Google DeepMind). It reports Carbon first on version 5.0 of the Artificial Analysis Intelligence Index at 59, and leads on three cybersecurity benchmarks it cites: finding software flaws (SEC-bench Pro, 88.2%), exploiting them (ExploitGym, 54.8%) and patching them (CWE-bench, 71.0%). It resists every attempt on an indirect prompt-injection test (0.0%), against 0.7% for the earlier Argon. Pricing matches Argon at $2 and $10 per million input and output tokens.

Why it matters: Carbon continues the Gemini 4 line the record already tracks, but where Argon shipped with science case studies and a free-access plan for scientists, Carbon's stated focus is cybersecurity and long agentic tasks. Google is gating it: cyber defenders first through its Fairwind programme, developers and consumers later with no date, and it says it is in the US government's voluntary pre-release testing. The rollout and the government testing are the governance step to watch as these models gain offensive and defensive security skills.

Evidence and limits: Every benchmark figure is Google's own, with no independent check and no technical report cited. The internal coding and secure-code gains are on its own teams and tests. Access is restricted, so nobody outside the vetted partners can test the claims.

What happened: Eunbi Rha, Mingda Li and colleagues at MIT built QUANTA: an agent that draws on literature and past runs, plans transport measurements on a Physical Property Measurement System and revises the plan as data arrive, while a rule-based layer checks every command against hard limits. On the Weyl semimetal TaP it recovered from an amplifier overload, chose a current that passed its noise checks and rejected the faster averaging setting, though none fully passed. On Co2MnGa it tested a nonlinear Hall signal's origin in four steps.

Why it matters: Measuring a quantum material means many small calls, such as which current, how long to average, and whether an odd signal is the sample or the setup, and that know-how mostly lives in people. Here an agent makes those calls on a real instrument and real samples, with the reasoning for each step logged. It also stops where the data stop: it left the Co2MnGa signal's origin open, electronic or thermal, because the fit could not tell the two apart.

Evidence and limits: One instrument, two samples, run by the team that built the agent, with no comparison against a researcher doing the same campaign. People still mount samples and change wiring, and high-risk actions need human sign-off. The agents ran on OpenAI's Astra Ultra model. It cleared the overload by widening the amplifier's input range, without finding the cause. Neither campaign produced a new physical finding. A preprint.

By Eunbi Rha, Mingda Li

What happened: Juan B. Pérez-Sánchez, Varinia Bernales, Alán Aspuru-Guzik and colleagues at the University of Toronto, with co-authors in Chile and Spain, had El Agente Quntur, a team of agents built on OpenAI's GPT-5.6 models, choose the active space, the small set of orbitals a multireference calculation treats in full, for 32 excited states of 31 molecules. Against published CASPT2 values the agents' energies were off by 0.239 eV on average, against 0.459 eV for the best of six fixed Active Space Finder protocols.

Why it matters: Choosing the active space is the step in these calculations that still takes an expert, and it is why some chemists say the methods are not a proper model chemistry. Here the agents did more than pick orbitals: they checked which excited state a calculation had actually found, noticed orbitals lost during optimisation and changed course. The prompts, agent traces and calculation files are in a public replication dataset, so the decisions can be audited.

Evidence and limits: The reference is another calculation, CASPT2 from earlier papers, not a measured spectrum. For four cases the agents computed a different excited state from the reference, which the average does not penalise, and case by case the agents and the best fixed protocol each won 16. The 32 cases used 764 million tokens, searches included, and repeat runs of one prompt took different routes.

By Juan B. Pérez-Sánchez, Varinia Bernales, Alán Aspuru-Guzik

What happened: BigHat Biosciences and Anthropic ran two lead-optimisation campaigns on BigHat's Milliner platform, about 1,010 designs over seven rounds in all, four on one molecule and three on the other, with Claude in the loop. Reading the raw instrument curves, they found a thermal measure on each scaffold did not measure what its name implied: on the nanobody, the clumping-onset temperature. They fitted a physical model of the curves, chose new measures and changed the protocol. Both leads beat their parents on binding, heat stability and clumping.

Why it matters: A closed-loop lab optimises the number it is given, so a measure that stands in badly for what a drug needs cannot be rescued by a better model or more data. The gain here came from people and a model reading the instrument signal and changing the experiment, not from more rounds of search. Published lab-in-the-loop work mostly takes the reported assay value as a correct label; this campaign did not.

Evidence and limits: Both companies' own report, not peer reviewed, every measurement in BigHat's lab: two molecules on one platform. The report says each lead's values rest on one or two replicates and that confirmation runs come first, and the new measures were chosen inside the campaign that produced them. The leads are antibody fragments, not drugs, and nothing is tested in animals or people.

By Samuel Stanton, Peyton Greenside

What happened: Over three rounds of design and measurement with AI agents, the company built a 45-amino-acid signalling tail for a CAR, less than a third the length of the 4-1BB-CD3zeta domain in approved therapies. Against that domain it raised receptor levels on the cell surface and antigen-driven IFN-gamma release, with none detected when antigen was absent; it kept killing going longer after mRNA delivery, carried four receptors on one mRNA dose, and depleted more B cells in donors' blood cells after lentiviral and lipid-nanoparticle delivery.

Why it matters: Shortening the tail was expected to cost function, because more activation motifs were read as stronger signalling. The short tail made more receptor per unit of mRNA, which is why the team could put four receptors on one dose, and the report suggests compact tails may lower the dose needed to make CAR T cells inside the body. It is also a worked case of agent-guided design: the agents proposed designs and revised them as measurements came in.

Evidence and limits: The company's own technical report, not peer reviewed, and all of it in cells: human blood cells from a few donors, no animal test. The report says how compact tails perform in a living animal, and how many cells carry all four receptors, remain unknown; exhaustion was not measured, and whether agent-guided design beats expert design, or needs fewer rounds, is untested against a matched baseline.

By Liam Bai, Wujie Wang, Jason Yi

What happened: Enveda tested CDP, its Claude-based checker for drug manufacturing batch records, on four executed records from one clinical campaign: 1,061 largely handwritten pages. Human reviewers had raised 11 actionable issues; CDP raised nine as direct matches and two judged intended matches, across its runs rather than in one pass. Single runs on Claude Opus 4.7 added 7 actionable comments no reviewer had raised. A review holding CDP's comments took 9.4 times less time.

Why it matters: Batch-record review is required before a batch of a drug is released, and Anthropic's post says Enveda's manufacturing chemists had been spending up to 75% of their time on paperwork. This is the regulated-paperwork end of drug development, which rarely gets measured at all, and the yardstick here is what human reviewers caught on real records rather than a score on a benchmark.

Evidence and limits: The company's own report, not peer reviewed: four records from one campaign, checked against reviewers' comments, two of the four reviewer arms being outside consultants. No CDP comment cited content the records do not contain, and each run also posted one misread at Major, and the human reviewers could consult earlier-step records CDP was not given. Whether the 7 extra comments matter is Enveda's judgment. The time figure comes from a review holding CDP's comments in advance.

By Andrew Martin, Brandy Schenck, Daniel H. Wee, Viswa Colluru

What happened: On its Science page Anthropic said it has acquired Mithril Medicine, which was developing treatments for two rare childhood muscular dystrophies, and that its founder, Cody Tranbarger, has joined the company to continue that work and start further programmes. The same post reports research partnerships with six organisations: BigHat Biosciences, Bristol Myers Squibb, Divergence Labs, Enveda, Generate:Biomedicines and The Column Group, which is setting up a company, Pleiades, to run drug programmes with Claude.

Why it matters: A model supplier has become a drug developer. The partnerships put Anthropic's own scientists inside other companies' programmes, and the acquisition gives it programmes it owns, so it now carries the risk and would hold the result. The post also says Anthropic plans to scale this work up substantially.

Evidence and limits: No terms were disclosed for the acquisition. Anthropic says that if the candidates continue to hold up it expects a drug to be ready to start human trials as soon as late 2028, which is its own forecast, and the mouse results behind it are in a technical report it published itself, not peer reviewed; that report studies two tool molecules in a pilot study, not a candidate. Pleiades has no disclosed programme yet.

What happened: Omalizumab, the only approved biologic for food allergy, neutralises circulating IgE but does not effectively remove the kind bound to FcεRI on mast cells. Guided by its research team, Claude ran 22 phases toward a two-armed antibody that would prise it off. Of 91 disruptor designs screened at one concentration, 20 beat every negative control; eight were purified and titrated, three of them removing more than omalizumab from 25 to 400 nM. Three of 57 anchor designs bound IgE, at apparent affinities of 3.4 to 235 nM.

Why it matters: Pulling bound IgE off the receptor needs a precise geometry, which display and immunisation libraries cannot specify, so designing from a structure is the point here rather than a convenience. Activity survived as many as eight substitutions from the parent, and hits came from every design scope and method the campaign tried. Divergence says the two arms came in weeks rather than the months screening takes.

Evidence and limits: Divergence's own report, not peer reviewed, all biophysics on a sensor chip: no cells, no animals, the two-armed molecule not yet made. In the titration the report says the variants did not appear to improve on the parent sdAb026, the best of them matching it at most concentrations, and Anthropic's post summarising the work does not mention the parent. The anchor affinities may be confounded by avidity, and the method needed an existing disruptor's sequence and solved structure.

By Ezra Erives, Emilio Ferrara, Bowen Jing

What happened: The model generates video together with music, ambient sound and interactive dialogue, which DeepMind calls its first real-time world model to generate audio natively, and it takes natural-language edits while a simulation runs, to change the weather, move the camera or add a person. It builds on the Gemini family, with Gemini Omni anchoring real-world appearance, and every output will carry a SynthID watermark.

Why it matters: DeepMind's stated purposes include developing training environments for robots and agents, and testing AI systems in different environments before taking them to the real world. One clip is generated from the prompt "Universal Robots UR5 holds a pipette and dispenses liquid into a lab plate, triggering tiny electric sparks", the only lab scene in a post whose testers are creative industries, journalism, education, gaming and engineering.

Evidence and limits: No benchmark, score or comparison with Genie 3 appears: it is clips, captions and an internal safety test with no result given. The two-minute consistency figure in press coverage is in neither post; neither gives a session length, and the post says object permanence can drift the longer a simulation runs. Trusted testers only, no public access.

By Raia Hadsell, Diego Rivas, Hang Qi

What happened: In a bioRxiv preprint, Paride Antinucci, Dominic Hall and colleagues at Biographica, a London crop-AI startup, feed a model a gene's DNA plus the measured levels of transcription factors, the proteins that switch genes on, in each tissue or condition. On held-out data from six plant species its median per-gene correlation across contexts is 0.831, against 0.486 for the same model without that input, and 57-246% above three fine-tuned genomic foundation models. It then designed edits of up to 20 DNA letters in soybean and tomato.

Why it matters: Many crop genes affect more than one trait, so an edit that helps in one tissue or condition can cause unwanted changes in others. A model that predicts expression per context lets a breeder rank candidate edits by the change wanted in one place and the change everywhere else, before any plant is edited. Biographica sells gene-editing targets to seed companies.

Evidence and limits: The edits exist only as predictions: none has been made in a plant and measured. The test data come from the public PlantExp collection and were scored by the company's own team, and the preprint is not peer reviewed.

By Paride Antinucci, Dominic Hall

What happened: Deputy Prime Minister Gan Kim Yong announced MARVEL-AI at SWITCH 2026: a platform led by A*STAR, starting with S$150 million of government money, in which AI models pick candidate materials and design experiments and self-driving labs make and test them. It is anchored at A*STAR's Institute of Materials Research and Engineering, whose executive director, Xian Jun Loh, also leads MARVEL-AI. Named partners include ST Engineering, Heraeus Electronics and Foxconn Industrial Internet on materials, and CuspAI and Google Cloud on the AI side.

Why it matters: The speech names the gap it is meant to close: models such as Google's GNoME and Microsoft's MatterGen propose materials, but a material still has to be made, tested and manufactured before industry can use it. MARVEL-AI puts the models and the labs in one national programme and opens its equipment, testing and researchers to companies, startups and SMEs included. CuspAI, which designs materials with AI, will connect its system to the platform's automated labs.

Evidence and limits: This is an announcement: the speech and reports give no split of the S$150 million, no timeline and no material yet. The figure of up to 470 alloy samples a day, more than 400 times conventional throughput, describes an existing lab process Enterprise Singapore cited, not a MARVEL-AI result.

What happened: Adam Lahouari, Jutta Rogal and colleagues at New York University and the Flatiron Institute studied acridine, a small molecule with at least eight crystal forms. Melt experiments solved the structure of form VIII for the first time. Their CrystalMath method kept 15 of 341,683 generated structures without computing any energy, and the 15 include all seven forms with known structures, form VIII among them. A MACE potential fine-tuned on acridine, with full free-energy calculations, reproduced the switch from form II to form III as temperature rises that experiments show.

Why it matters: One molecule can crystallize in several forms that behave differently, which matters for drugs, organic electronics and agrochemicals, and crystal-structure prediction with machine-learned potentials is how the field hopes to rank the forms before making them. Here the ranking step needed tuning: four off-the-shelf foundation potentials put the forms in the wrong order, and simpler free-energy approximations missed the observed crossovers. The study also grows acridine from the melt with 18 additives and finds the two strongest effects match computed defect energies.

Evidence and limits: One molecule. The predicted crossover between the two most stable forms sits near 290 K, against about 318 K measured, some observed transformations disagree with the free-energy curves, and form IV was left out of them. The additive link rests on two of the 18 additives. A preprint.

By Adam Lahouari, Jutta Rogal

What happened: Edgar Olehnovics, Matteo Salvalaglio and colleagues at University College London, AbbVie and the University of Colorado Boulder train a normalizing flow that maps molecular-dynamics snapshots of a crystal onto a simple reference state, which gives its free energy directly, without the chain of intermediate simulations the Einstein crystal method needs. On succinic acid and two drug molecules, Veliparib and Mivebresib, the results agree with that method, in crystals of up to about 3,000 degrees of freedom; across all systems it needed about 2.5 times less computing time on average.

Why it matters: Crystal-structure prediction can propose hundreds of possible packings for one drug molecule, and ranking them by stability at room temperature needs free energies. The standard method is accurate but too slow to run across a whole landscape. A method that needs one ordinary simulation per crystal form makes that ranking cheap enough to automate, and because it needs fewer energy evaluations it suits slower, more accurate force fields.

Evidence and limits: Checked against another computed method, not against experiment. The ranking depends on the force field: for Veliparib a generic force field and a tailor-made one disagree. The speed-up is uneven: for succinic acid it was faster only in the smallest crystals, and exact runtimes were not recorded.

By Edgar Olehnovics, Matteo Salvalaglio

What happened: Huifeng Zhao, Tingjun Hou, Yu Kang and colleagues at Zhejiang University and Peking University built GlycoWeave, which predicts the shape of a sugar chain bound to a protein by adjusting its bond torsions rather than moving atoms freely, which keeps the sugar's chemical structure intact. On 698 recent protein-sugar complexes, 71.9% had a high-quality pose among its top 25, against 19.2% for AlphaFold 3 and 16.9% for the docking tool Vina-Carb. Each pose took 0.34 seconds.

Why it matters: Sugars on proteins shape immune recognition, infection and how some drugs act, but they are flexible and thinly covered in experimental structures, so general structure predictors handle them poorly. Here only 41.0% of AlphaFold 3's top predictions passed the structural-validity check, against all 698 of GlycoWeave's (the docking tool Vina-Carb reached 99.7%). A model built for one awkward class of ligand measures how much accuracy a general co-folding model gives up on molecules unlike its training data.

Evidence and limits: Checked against known experimental structures, not new wet-lab work. The headline compares the top 25 poses; the paper's main text does not state AlphaFold 3's own sample count for that comparison. The protein pocket is given as input, so this is not blind docking. A preprint.

By Huifeng Zhao, Tingjun Hou, Yu Kang

What happened: Amaç Herdağdelen, Hailey Joren and Alex Tamkin had 50 analysts trained in causal inference judge a fictional farm programme with Claude Code. On each of four choices with two reasonable answers, such as how to measure productivity, a coin flip set which way the agent leaned, unknown to the analysts. They matched the lean 86% of the time, against 50% if it made no difference. Afterwards 77% gave the agent's arguments as their reasons in at least one answer; 15% credited it.

Why it matters: As agents take over the hands-on work of an analysis, the choices left to the researcher, what to measure and how to compare, are where judgment enters. This trial measures how much of that judgment moved to the agent: the coin flips alone predicted 82% of final recommendations, against 61% by always guessing the commonest one. It bears on any lab using agents to analyse experiments, and on review, since a reviewer working with an agent is an independent check only if the calls are their own.

Evidence and limits: The task was fictional and the agent was told to argue its lean, so real agents may sway people less. The 11 who worked without the agent made more serious errors, but they were fewer and less experienced, so the comparison is weak. The study is Anthropic's own.

By Amaç Herdağdelen, Hailey Joren, Alex Tamkin

What happened: The S-1, filed on 26 October, shows $16.3bn of revenue for the first half of 2026 against $4.6bn for all of 2025, and an operating loss narrowed to $693m from $2.1bn. The $80.7bn net loss is almost all a non-cash $72.4bn remeasurement of convertible notes, derivatives and SAFEs. Compute commitments include at least $114.5bn with Google, $110bn with Amazon and $44.9bn with Microsoft, owed regardless of usage.

Why it matters: For science, the filing says frontier AI 'may extend' to robotics and to scientific research, including drug discovery and materials science, and that Claude with the Model Hardware Standard can 'now' operate microscopes, liquid handlers and robotic arms. Its vision of 'Powerful AI' adds tasks running for weeks and 'compressing decades of scientific progress into years'. In the accounts, though, there is one segment, and the life sciences lab appears among the risk factors.

Evidence and limits: The June figures are unaudited interim ones. The three cloud commitments are three of seven named, with $125bn to $150bn more TPU compute estimated outside the table, and the aggregate line is blank. The top five customers were about a quarter of revenue. A Founder LLC's new share class carries 50.1% of the vote, and the safety trust's own class elects a majority of the board.

What happened: Jared Lewis's group at Indiana University, with David Baker, tested 96 designs; 63 expressed, and one scaffold called DE3 bound one mirror image of an iridium photocatalyst at 94% enantiomeric excess. As designed it gave low selectivity in catalysis. Screening mutations reached a 3:97 enantiomeric ratio, with turnovers above 50,000 in air and the protein reusable over three cycles.

Why it matters: Generative design supplied a pocket that holds a cofactor nature has no protein for, which is what the field could not get from existing folds. On this reaction the enantioselectivity came from screening mutations afterwards; a second design campaign later reached 90:10 on a different reaction with no evolution at all. Metal polypyridine photocatalysts had resisted both routes until now.

Evidence and limits: One scaffold carried through, out of five folds tested for binding. The pocket was designed around a ruthenium cofactor and bound only iridium analogues, and no cofactor-bound structure was obtained. Other reactions in the scope are only moderately selective. Designed and evaluated by the same groups. An accelerated preview.

By Sandip Mishra, David Baker, Jared C. Lewis

What happened: OpenAI's manuscript, dated 25 October and added to its public repository the next day with a Lean formalization, gives a deterministic algorithm that counts the satisfying assignments of fixed-width CNF formulas in subexponential time. Anthropic's 49-page paper, dated 26 October and pushed on the 27th, claims the same thing and prints its author as Claude, Anthropic. It dates its model's discovery to 16 October.

Why it matters: The hypothesis has organised decades of lower-bound work: Anthropic's paper says a literature search by Claude found more than 800 papers proving a result of their own only under it or something stronger. If either proof holds, those conditional results have to be read again, and Ryan Williams, who is helping organise the review, says that if true it should move every computer scientist's weight a little toward P=NP.

Evidence and limits: Nobody outside either company has verified either proof. Lean checks that a proof follows from a formal statement, not that the statement means what the paper claims; Anthropic writes that down and adds that at the time of writing no person is known to have checked its Lean. OpenAI's scope note formalizes a weaker bound than its manuscript, on canonically encoded inputs, still subexponential. Anthropic calls it purely asymptotic.

What happened: OpenAI told three complexity theorists it had the result and would release it, and asked them to organise its examination. Russell Impagliazzo and Ramamohan Paturi at UC San Diego and Ryan Williams at MIT wrote to their field the same day: "Our involvement should not be taken as certification of the claim." Williams said on his own account that the group signed no NDAs and takes no pay, and that OpenAI agreed to release its raw data and sponsor a conference.

Why it matters: Who certifies a machine's result is what the last six weeks have been about, and here a lab named its examiners before releasing the claim. The letter is them refusing the role that implies while keeping their leverage: Williams says they asked for the raw data and a sponsored conference and OpenAI agreed, and the letter says the effort should confer ownership on no company and no chosen few, themselves included.

Evidence and limits: OpenAI's announcement page is 403 here, so its own account is not quoted; its history file calls the three an independent working group, naming no others. The physics result Al Jazeera reports as deferred has no public artifact, and OpenAI's 721-manuscript maths catalogue carries no quantum chromodynamics paper. The NDA, payment, data and conference points are Williams's words, not the letter's, which is undated.

What happened: OpenAI's 59-page paper, its author line reading OpenAI, dated 25 October and added to its public repository the next day, proves Sogge's conjecture for every spatial dimension at least two, with arbitrarily small Sobolev loss and none at p=2. From it the paper deduces the Bochner-Riesz conjecture, sharp bounds for Stein's square function, and the Kakeya and Nikodym maximal conjectures. OpenAI says it is releasing the model's output directly.

Why it matters: The published record had the plane from Larry Guth, Hong Wang and Ruixiang Zhang, and three-dimensional Kakeya from Hong Wang and Joshua Zahl last year; this claims every dimension at once, and cites two OpenAI preprints from September that it supersedes. Unlike the complexity result released the same day, the Lean here covers the headline claims: the local smoothing bound in every dimension, and that a set holding a unit segment in every direction has full Hausdorff dimension.

Evidence and limits: Nobody outside OpenAI has checked it, and OpenAI's own repository calls these manuscripts raw research data rather than polished papers. The restriction estimates for the sphere, paraboloid and cone hold only inside the conjectured range, so that conjecture is not settled here. The same repository withdrew three manuscripts on 7 October over a sign error.

What happened: A Roche team at Prescient Design and Large Molecule Research used a language-model pipeline to pull 89,074 antibody-antigen records out of 10,110 US patents, then built 11,247 pairs: an antibody a patent says binds, with no solved structure, against a sequence-similar decoy. Six structure models were scored. Protenix-v2 led at 72% of pairs and 87% on its most confident half, and picked the true binder out of 50 candidates 29% of the time. The best model touched a listed epitope residue in only 62.8% of cases.

Why it matters: Teams screening designed antibodies read a model's folding confidence as a stand-in for binding. This puts a number on what that is worth: much better than picking at random, nowhere near enough to name the one candidate to make. And telling a binder from a decoy does not mean the pose is right, so reading an epitope off these models is a separate claim these numbers do not support.

Evidence and limits: Nothing here was assayed: the binders are what patents claim, the non-binders are repertoire antibodies assumed not to bind, so the accuracy is against a constructed negative set rather than measured affinities. The structures were not in the models' training data; the patent text may have been. The preprint is CC BY and says the dataset is released, without stating its licence. Not peer reviewed, and the full text was not read.

By Daria Tupikina, Andrea Roncoli, Alexander Bujotzek, Brennan Abanades

What happened: Junkil Park and Aron Walsh at Imperial College London gave a composition-only synthesizability model a second task: rank known materials by the year each was first reported in the ICSD crystal database. On about 10,000 held-out reported materials it recovered 52.4%, against 47.0% for the model it extends and 48.4% with the years shuffled, all at one calibrated threshold where chance is 8.1%. Trained only on materials reported up to 2018, it put 7 later-reported compositions in its top 100, against 3 for the baseline.

Why it matters: Generative models now propose candidate materials far faster than labs can try them, so the question is which to make first. Synthesizability models usually treat every reported material as an equal example. This one uses the reporting order as free extra supervision, on the authors' reading that the order carries which precursors, methods and applications made each material reachable. The shuffled-year control is what ties most of the gain to the order itself rather than to the added task.

Evidence and limits: All checks run on existing databases. The authors say learning from history may favour chemistry already studied and score unexplored families lower. It reads composition only. The authors say the largest gaps come in the top 100 at the later cutoffs, where the statistical uncertainty is also larger. A preprint.

By Junkil Park, Aron Walsh

What happened: Sarath Menon, Braulio Sánchez Ortega and Ralf Drautz at ICAMS, Ruhr University Bochum, add a linear correction on top of GRACE, a foundation interatomic potential, fitted to free energies computed in simulation so that phase transitions land on the assessed SGTE values. Across 11 metals and 14 transitions, every corrected temperature falls within the set tolerance: 15 K for melting, 50 K for solid to solid. Gold melted at 844 K in GRACE and at 1346 K after correction. For titanium it took about 55 H100 GPU hours.

Why it matters: Foundation interatomic potentials learn from DFT data, and an error of a few meV per atom in which phase is more stable can move a melting point by hundreds of kelvin. This layer fixes the balance between phases without touching the base model: it needs only the energies and structures the model produces, not its parameters or training data, so in principle it fits any potential, open or closed. Copper's melting curve, fitted only at zero pressure, then follows measurements within 15 K up to 4 GPa.

Evidence and limits: The corrected temperatures are fitted to the SGTE values, so hitting them is calibration, not prediction, as the authors say. The layer can only rebalance phases the base model already describes and cannot add missing physics such as magnetism. Where the correction is large it moves other properties too, mostly towards experiment: liquid gold and silver become 3 to 6% denser, but vacancy energies move away from experiment for vanadium and aluminium.

By Sarath Menon, Braulio Sánchez Ortega, Ralf Drautz

What happened: Ruichen Li, Ji Chen, Weiluo Ren and colleagues at ByteDance Seed and Peking University split a real-space wavefunction into a spatial part, computed by a neural network (LapNet), and a spin part built to carry one exact total spin. Earlier neural network methods added a spin penalty to training, which can leave the wavefunction stuck mixing spins. The method matched experimental singlet-triplet gaps of biradical molecules about as well as the penalty method, gave an antiferromagnetic spin ladder for [Fe2S2] and a spin gap for an [Fe4S4] cluster.

Why it matters: Iron-sulfur clusters are central to biological nitrogen fixation, and their many spin states of nearly equal energy are where standard methods such as DFT, and even CCSD(T), can go wrong. With spin built in by construction, a calculation cannot drift into the wrong spin state, and chemists' own ideas (core and active orbitals, a first guess at how the spins couple) can be written into the network. With 10 active orbitals it reproduced the local spin trend on [Fe2S2(SCH3)4] that DMRG needed 32 to show.

Evidence and limits: Everything is computed. The iron-sulfur results have no experimental reference: they are compared with other calculations (FCIQMC, DMRG), and for [Fe4S4] the authors say the order of the three methods' gaps is not tied to any one cause. The November 2025 preprint called that gap more accurate; the published version drops the claim. Only the biradical gaps are checked against experiment.

By Ruichen Li, Ji Chen, Weiluo Ren

What happened: The Cabinet Office announced the AI Biosecurity Lab, an independent non-profit it calls the world's first dedicated AI biosecurity organisation, meant to detect biological threats earlier and speed up vaccines and other countermeasures. OpenAI is putting in £100m of compute and model credits through its Rosalind Biodefense programme, Anthropic up to £100m in credits, access and support. Google DeepMind and Basecamp Research join as research partners, not funders.

Why it matters: The £300m headline is £100m of public money; the rest is credits and access rather than cash, and the research partners give access, not money. It also puts frontier labs inside a national function: a National AI Biosecurity Initiative puts the lab with UKHSA, APHA and Dstl, and informs the MHRA's regulation of AI-designed medicines.

Evidence and limits: No budget breakdown, no timeline and no named staff; the scientific advisory committee is still to be announced. Anthropic's £100m is an 'up to' figure, and the page gives no period for either.

What happened: A Toronto, NIST and KAUST team compared deep-ensemble uncertainty against loss-landscape estimates: ALIGNN on band gap and formation energy, a multilayer perceptron on solubility. Calibration methods generalised only variably from in-domain to out-of-domain data, and calibrated uncertainties generally failed to cut the data an active-learning campaign needed on out-of-distribution data. Random forests and XGBoost on the same tasks showed the same poor uncertainty.

Why it matters: Active learning picks the next sample by uncertainty, and a campaign aimed at new chemistry is out of distribution by construction. The same failure in tree models as in the network points at the data: at least partly intrinsic to it, the authors say, rather than model capacity alone.

Evidence and limits: All computed, on three public property tasks. The claim is about these architectures and calibration methods on these datasets, not a proof that no calibration can work. Peer reviewed.

By Ashley S. Dale, Jason Hattrick-Simpers

What happened: Bristol Myers Squibb led; the company puts the post-money valuation at about $3B, up from $1.6B at its $365M Series D in May 2025. Pathos, a New York company co-founded in 2022 by Eric Lefkofsky and Ryan Fukushima and run by CEO Iker Huerga, licenses drugs other companies made and uses its agent platform, Foundry, to pick patients and adjust trials. BMS gets non-exclusive access to Foundry and three-year exclusive rights in neuroscience; Tempus will build a neurology patient dataset that Pathos licenses for $55M over five years.

Why it matters: Pathos puts its AI on the decisions that decide whether a trial succeeds: choosing which existing drug to run, in which patients, and when to change the trial. It does not design molecules. A drugmaker leading the round and taking access to the platform shows that one large pharma company will put money behind this use of AI, and the deal carries the method from cancer to Alzheimer's, a field with long trials and many failures. Simon Davies, who heads data and quantitative sciences at BMS, spoke for it in the announcement.

Evidence and limits: The round, the valuation and the deal terms are all from the company's two releases of 23 October; neither names the other investors or says whether BMS pays for its access beyond its stake. That Foundry identified JSKN016, an antibody-drug conjugate licensed from Alphamab, and has improved any trial's odds is the company's own account: no trial result from its approach has been published.

What happened: Bo Wang, Ci Chu and colleagues describe X-Automate in a companion post to X-Scientist: an off-the-shelf six-axis arm with a camera on it, machined grippers and control software written in-house, directed by the same agent system. Its first target is an Agilent HPLC, which has no motorised door and a drawer that shifts when touched. Over seven days in the company's own lab, a mobile robot and the arm loaded and analysed 18 plates, holding the HPLC busy 97.2% of the time against 53.6% in its model of weekday hours.

Why it matters: Protein-design groups can now make thousands of candidates a week, and the measurement that tells them which ones worked is the step that waits for a person to open a door. Xaira's answer is vision on the arm rather than taught positions, which is what lets one arm move between instruments that were never meant to be automated, and it sidesteps the instrument maker's paid interface. The company says adapting the arm to another instrument now takes weeks rather than months.

Evidence and limits: A company blog post, no paper, with the company reporting its own demonstration and calling the week a technical proof of concept. One instrument, one week, and the 80% is against a schedule the company modelled rather than ran. Four interruptions in plate storage upstream needed a person to recover them, with no errors it attributes to the arm or the agents. The plates were analysed but no scientific finding is reported.

By Bo Wang, Ci Chu

What happened: Hongjun Xiang and Xingao Gong's group at Fudan built an equivariant graph network that predicts both the charge density and the projector augmented-wave augmentation occupancies. Earlier universal models gave only a pseudo charge density, which cannot rebuild the electronic structure consistently. A trainable radial basis and a delta-learning step for charge transfer make it 50 to 150 times faster than earlier charge-density models; over 1,000 unseen materials its median band-energy error is 0.045 eV.

Why it matters: Charge density is what density functional theory solves for, so a model that reproduces it at the accuracy real codes use can stand in for the expensive self-consistent step rather than just predicting one property. The augmentation occupancies are what makes that possible here.

Evidence and limits: All computed, by the authors. The universal model covers non-magnetic structures. The speed is measured against two other machine-learning models and plane-wave DFT on one GPU. Peer reviewed.

By Xiwen Li, Hongjun Xiang

What happened: A Hong Kong University of Science and Technology team matched the crystals of 55 databases by structure into one set of 14.6 million crystals and 131.8 million property values, only 4.5% of whose crystal-property pairs carry a value. Given 100 verifications each, MatterGen conditioned on 400 GPa returned 100 stiff hits against screening's 55; but every generated hit contained osmium, 82 repeated a recorded composition, and screening's best hit was stiffer, 433.8 against 375.4 GPa.

Why it matters: A novelty share measures the reference set as much as the generator, and the usual reference is Materials Project alone. Against all 55 sources, GNoME's matches go from one to 18,234, its new share from 100% to 96.7%. The forward screen turned up 53,813 ultra-incompressible candidates with no recorded bulk modulus; 62 have been made, among them ReN2, measured at 428 GPa.

Evidence and limits: All computed, plus a literature audit: ReN2's 428 GPa is a published measurement, not a new experiment here, and the screen had predicted 349 GPa, 18% below it. The MatterGen runs are the authors' own, from released weights. A preprint.

By Zhilong Song, Zijun Pang, Lixue Cheng

What happened: An EPFL and Fribourg team fine-tuned the PET-MAD potential on r2SCAN data and ran large-scale molecular dynamics on Li6PS5Cl, screening carboxylic, sulfonic and phosphonic acid coatings by chain length and coverage. Simulation said lithiated head groups adsorb more strongly and that long-chain lithiated carboxylates form dense brush-like hydrophobic layers, with protection setting in near 8 Å. Infrared, impedance and H2S measurements followed.

Why it matters: The loop half closes: simulation set the chain length, and the H2S measurements run the same way, longer carboxylate chains suppressing it better. It also names the conflict rather than hiding it, since a thicker coating blocks lithium transport, so the best coating is a middle length.

Evidence and limits: One electrolyte, Li6PS5Cl, and one potential fine-tuned by the authors. The 40 to 55% figure is what is left after processing, not a gain. The experiments cannot rank the head groups, and the paper says so. A preprint.

By Zhiyi Wang, Leonie Braks, Hanna Türk, Davide Tisi

What happened: Tetsuya Yamada and Henrik Kaessmann at Heidelberg, with Svante Pääbo at the Max Planck Institute for Evolutionary Anthropology, predicted the regulatory effect of every single-nucleotide substitution on the human lineage across 305 tissues and cell types. Most of the 71,345 gains sit outside the regions evolution researchers had flagged as fast-changing, and more often than not they come from a regulatory element being reused in a new tissue rather than a new one appearing.

Why it matters: The question of which non-coding changes made humans human has been stuck on data nobody can get: great-ape tissue panels are thin, and no functional measurement is possible on an extinct hominin at all. A sequence model sidesteps that, because it scores a reconstructed ancestral allele as readily as the living one. The payoff here is the archaic comparison: nearly every gain now fixed in humans was already there before the split, so the modern-human-specific ones are few, and the paper names which ones move gene expression most.

Evidence and limits: The map is prediction, not measurement. Where it could be checked, against accessibility measured in four great apes' cell lines, AlphaGenome beat Enformer and ChromBPNet and 82.7% of its strongest calls held up, against 31.1% of controls, but it recovers only a quarter to a half of the measured human-specific changes depending on the threshold, and misses clustered gains more often than isolated ones. A preprint.

By Tetsuya Yamada, Henrik Kaessmann

What happened: Jihan Zhou's group at Peking University put a two-stage network after the reconstruction step: a ResUNet-3D variant corrects distortions across the volume, then a set transformer reads the shape of the electron density around each atom to name its element. Trained on 17,340 simulated samples and benchmarked on 42,588, at typical noise it cut the median coordinate error from 0.244 to 0.100 angstrom and raised atom recovery from 93% to 99%, elements called right 99.5% of the time.

Why it matters: Electron dose is what has kept atom-by-atom tomography to materials that survive the beam, which leaves out most of what people want mapped. The gain here is in post-processing rather than in the microscope, so a tilt series already sitting on disk can be re-read: thinned to a sixth of its dose, a published Pd@Pt series matched the full-dose conventional reconstruction on peak sharpness and repeatability.

Evidence and limits: No new experimental data were taken for this paper: the one demonstration on real data thins a published series, halving its projections too. Every other number is simulated, and the claim that this reaches beam-sensitive materials such as perovskites rests on simulation alone. Peer reviewed.

By Yao Zhang, Lanyi Cao, Zhen Sun, Jihan Zhou

What happened: A KAUST and Karolinska team ran image-based, transcript-based and multimodal segmenters, from Watershed to Cellpose, Baysor and three versions of Segment Anything, over 15 datasets from 5 platforms and 12 human and mouse tissues. On their overall score Segment Anything led at 0.58 and Watershed came last at 0.27, but count how many cells each found and SCS leads instead; the ranking also reorders by dataset: SAM did well where cells look distinct, SCS where they look alike.

Why it matters: Segmentation is the step every spatial result stands on, because it decides which transcript belongs to which cell. This benchmark shows the choice carries all the way through to cell-type proportions, how much signal bleeds between neighbours, and inferred ligand and receptor pairs. So a published spatial finding depends on a step with no standard, and the pipeline released here is an attempt to make the comparison repeatable.

Evidence and limits: There is no true cell boundary to score against, so the metrics are biological proxies: expression purity, marker genes, cell-type proportions against a reference. The authors say platform design, image quality and gene panels cannot be fully separated from the algorithms, and that their gene-set sensitivity test ran on one dataset only. Two of the 11, Swin-S and Cascade Mask R-CNN, were left out of the scored comparison. Peer reviewed.

By Xabier Martínez-de-Morentin, David Gomez-Cabrero, Jesper Tegner

What happened: Lightspeed led; Premji Invest, Mayfield, IVP, Lux, Conviction, Menlo Ventures and 12 other firms joined; angels include OpenAI's chief research officer Mark Chen and its CFO Sarah Friar. The South San Francisco company is a contract research organisation: it will make variations of a lead molecule for drug hunters, with chemists guiding the work. Its co-founders are CEO Kevin Weil, chief scientific officer Sol Reisberg (medicinal chemistry, Terremoto Biosciences), CTO Manohar Paluri (Meta Superintelligence Labs, a founding member of FAIR) and COO DJ Patil, the first US chief data scientist.

Why it matters: Weil's case is that AI has sped up software and mathematics but not chemistry, because chemistry moves only when someone makes a compound and tests it. A lab that sells that work to drugmakers also produces the experimental data better models need, and Warp says it will have no drugs of its own. The other half of the pitch is location: the release cites a 2024 industry survey in which 74% of companies had Chinese contracts for preclinical and clinical work, and the BIOSECURE Act signed at the end of 2025.

Evidence and limits: The round is the company's own announcement, which names no stage. The $865M post-money valuation comes from press reports citing the company and Weil, not from the release. Nothing is automated yet on the public record: the release describes labs being built and a first service guided by chemists, and the site names no customer, only a small number of design partners.

What happened: Google DeepMind and Google Research, with the World Resources Institute and IIASA, released Forest Typology 2020, which labels every 10-metre patch of land as one of six types, from primary forest to plantation, tree crops and other land. Built on AlphaEarth Foundations embeddings, it is 90.2% right overall against 8,190 expert-labelled sites, and is open on Earth Engine.

Why it matters: The EU rule turns on what a plot was in 2020, not just whether it had trees, and a tree-cover map cannot tell a natural forest from one logged and replanted as plantation. This map makes that distinction.

Evidence and limits: The 90.2% is carried by the large other-land class; per class, F1 runs from 82.9% for primary forest down to 58.2% for planted forest. Accuracy was checked on the Geo-Wiki platform of IIASA, a co-developer.

By Maxim Neumann, Anton Raichuk

What happened: Harsha Nori, Eric Horvitz and colleagues at Microsoft AI built a benchmark where a case starts from a short presentation and the diagnostician must ask questions and order tests, each with a cost, before naming a diagnosis. Their orchestrator, MAI-DxO, has language-model agents play a panel of doctors. It was right on 83.2% of New England Journal of Medicine clinicopathological cases and 80.3% of MIMIC-IV emergency admissions; generalist physicians scored 36.1% and 46.8%. Mustafa Suleyman, Microsoft AI's CEO and a co-author, called it a step toward medical superintelligence.

Why it matters: The benchmark, first posted as a preprint in June 2025 and now peer reviewed, tests diagnosis the way a doctor works: not picking an answer from a full case write-up, but deciding what to ask and which tests are worth paying for. Scoring cost beside accuracy lets a system be judged on whether it orders fewer tests, not only on whether it gets the answer. The Science version adds emergency-department cases and newer models.

Evidence and limits: The cases are published records run in simulation; no patient was seen. The doctors worked alone on cases usually handled by specialists, who were not compared. TechCrunch reports much of the gain comes from the underlying model: GPT-5 alone scored 78.9% on the hard cases, and on emergency cases the orchestrator added little.

By Harsha Nori, Eric Horvitz

What happened: Carbon-X, built by Hugging Face with Zhongguancun Academy and TIGEM in Naples, starts from Qwen3-14B, is trained on 302 billion more tokens (55% of them raw DNA), then tuned to answer questions about sequences pasted into a chat. On five DNA benchmarks it averages 66.7%, against 67.5% for GPT-6.1 Sol and 60.9% for Opus 5.5, and leads every model tested on coding-variant effects (74.9%) and next-base prediction. The weights are out under Apache 2.0 and run on one 80 GB GPU.

Why it matters: DNA models usually output scores a scientist has to interpret, while a chat model may explain what a gene does yet struggle to judge a change in its sequence. Carbon-X puts both in one open model a lab can run on its own hardware, so unpublished or patient sequences need not go to an outside API. The team estimates that scoring ClinVar's 2.4 million variants of uncertain significance would cost about $300 with Carbon-X, against $32,000 to $4 million with the frontier models it tested, a range that wide because it spans models and reasoning settings.

Evidence and limits: Every score is the team's own, and it built two of the five benchmarks. Nothing was measured in a lab or clinic, and the team says the model is not clinically validated. Frontier models still lead on insertions and deletions, on single-base variants in long sequences and on sequence-feature questions, where Carbon-X can also miscount bases. The costs are not like for like: Carbon-X at measured throughput on a $5-an-hour H200, the frontier models at list API prices.

By Loubna Ben Allal, Diego di Bernardo

What happened: The company's research note and preprint set out a four-step cycle: train compact experts on separate skills, break each answer into checkable requirements and accept an expert's contribution only where it beats the current one, write the accepted parts into the target model, then specialise again from it. In the 4B and 8B settings it ranks first among the methods compared on all five scientific benchmarks, scientific reasoning at 55.0% against GRPO's 53.0%. With 8B experts and a Qwen3.5-27B target, all seven benchmarks improve, the mean from 87.3% to 94.0%.

Why it matters: Most science models are trained once and then stand still, and a lab that wants a new skill in one trains a new model. The claim here is that a smaller model's strengths can be moved into a larger one, which the 8B-to-27B run is the test of, and that the filtering is what makes it work: without the verification step the same supervision drops scientific reasoning from 55.0% to 50.6%, and scattering it at random costs 6.3 and 12.1 points. The open 8B checkpoint lets someone outside the company check what the recipe produced.

Evidence and limits: Benchmark scores only, all of them the company's own, scored in-house and not peer-reviewed. The note says its own report does not show that repeated cycles keep accumulating gains, and the 27B run also changes model generation, which it reports separately; the model card says the open checkpoint renames and republishes an existing one rather than a fresh training run. The rankings are first among the methods compared, not against the field.

By Xun Xu, Zaixi Zhang

What happened: Michele Ceriotti's group at EPFL applied the Point-Edge Transformer, which drops explicit O(3) algebra for edge-to-edge attention, to molecular dipoles and polarizabilities, NMR chemical shielding tensors and auxiliary-basis expansions of the electron density. A bigger backbone improved accuracy and equivariance error together. It matches or improves on the released models at lower inference cost, the density only when trained on a better-posed target.

Why it matters: Strictly equivariant networks pay for their guarantee in tensor products that grow steeply with angular order, and can fail to represent a target if truncated. Targets like shielding tensors and basis expansions are where that bites. Learning the symmetry instead removes the trade-off, if approximate equivariance is good enough.

Evidence and limits: All computed, by the authors. Equivariance here is approximate rather than guaranteed, and the authors say only that it affects most observables little, not all. Electron density is the weakest case: its equivariance error grows with angular order, and above order two it falls with backbone size no faster than the symmetrized error does. A preprint.

By Joseph W. Abbott, Michele Ceriotti

What happened: Gus Hart at Brigham Young University and Jacob Pursley at the DOE's Kansas City National Security Campus tested 14 universal machine-learned potentials on 1.45 million ordered intermetallics against AFLOW, a database none of them trained on. For half the structures AFLOW, Alexandria and OQMD differ by under 6 to 8 meV per atom, but a minority differ far more, raising the average to 16 to 30. Re-referenced, the best potential agrees with AFLOW as closely as Alexandria does.

Why it matters: Leaderboards rank these potentials by formation-energy error against one database, as if it were ground truth. The disagreement between independent databases sets a floor under any such number, and the top models now sit at it. Screening out broken elemental references aligns the energy and stability rankings, and the leader's 7 meV per atom margin shrinks to 0.4 when both are scored at a common geometry.

Evidence and limits: All computed: this compares databases and models, with no new measurement. Intermetallics only, chosen as a region where DFT is relatively accurate. A preprint.

By Gus L. W. Hart, Jacob N. Pursley

What happened: Statisticians at Karlsruhe and Marburg put Pangu-Weather, GraphCast and Aurora against ECMWF's HRES under one experimental design. HRES came out ahead when scoring was restricted to events that actually happened; the AI models came out ahead under weighted scoring functions. A contingency table shows why: the AI models forecast extremes less often than they occur, so misses dominate their scores, while HRES forecasts them more often than the AI models do, so false alarms dominate its.

Why it matters: Published studies disagree about AI models on extremes, and this says the disagreement is largely in the protocols rather than in the models. The decomposition puts the score gaps down to calibration, not discrimination: every model tells an extreme from a normal day about as well, Pangu-Weather least of them, and the table shows the AI models are simply too conservative about saying so. That is a fixable problem, and a different one from not seeing the event.

Evidence and limits: All computed, against the analysis that initialised all four models, which the authors say slightly favours HRES; station recordings would test it. One physics model and three AI models, on the variables and lead times chosen here. A workshop paper at NeurIPS 2026, so shorter and less reviewed than a journal article.

By Tobias Biegert, Sam Allen, Zhongwei Zhang, Sebastian Lerch

What happened: Chengming Zhang and Luonan Chen, with Kazuyuki Aihara at the University of Tokyo, split the latent space of a hierarchical model into causal and spurious factors and apply do-calculus to the causal half, then recurse to rebuild the regulatory layers. On synthetic data, stem-cell differentiation, a CRISPR activation screen of 31 single and 26 paired conditions with some held out, a type 2 diabetes multiome and gut microbiome data, it beat the methods compared on causal triggers in most settings.

Why it matters: Most regulatory networks read off single-cell data are built from correlation, so a gene that moves with a state change is indistinguishable from one that drives it, and that is the difference between a target worth perturbing and one not worth the bench time. Encoding the intervention itself, rather than the association, is a different route to the problem, and the output is a ranked list of candidates to test.

Evidence and limits: All computed on public datasets, with no new experiment. The authors say the inferred triggers may still contain spurious biological signal, that the in silico perturbations are a qualitative read of direction rather than a predicted magnitude, and that simple additive baselines still do well on part of their own perturbation benchmark. Its results also depend on the regulatory hierarchy assumed. Peer reviewed.

By Chengming Zhang, Deyu Cai, Kazuyuki Aihara, Luonan Chen

What happened: A Nanjing University, CUHK and NUS team split the problem in two: generate an RNA shape in a pretrained structural latent space, then predict the contacts that place it against the protein, rather than generating shape and pose together. On 124 protein-RNA complexes held out by date, it leads ODesign, RFDpoly, RNAFlow and NACraft on complex recovery, with DockQ 0.131 against 0.116 for the next best. Twenty of the 124 clear DockQ 0.23.

Why it matters: RNA that binds a chosen protein is a route to reagents and to drugs, and unlike protein binder design it has stayed out of reach, partly because so few protein-RNA structures have been solved. What this paper pins down is where the state of the art stands when the binding site is withheld: five methods, and the best of them recovers the native pose on one target in six.

Evidence and limits: All computed, no synthesis and no binding measurement, and the authors say structural recovery establishes neither affinity nor activity. The headline ranking uses the native interface after generation to pick which of RiboBridge's own pockets is scored, so it is not a blind result. RiboBridge also clashes more than two of the baselines and does not lead on sequence recovery. A preprint.

By Zhou Zhang, Hanqun Cao, Tianfan Fu

What happened: In INTerpath-001, 1,137 patients with resected stage IIB-IV melanoma were randomised 2:1 to intismeran, an mRNA encoding up to 34 neoantigens chosen from each patient's tumour sequencing, plus KEYTRUDA, or to placebo plus KEYTRUDA. After a median 24.6 months, two-year recurrence-free survival was 82.2% against 70.0% (HR 0.58), and the risk of distant metastasis or death fell 38% (HR 0.62). The data go to ESMO 2026's Presidential Symposium and are accepted by NEJM.

Why it matters: Until now only the topline was public. The two arms part only after six months, once the course of up to nine vaccine doses is given: the hazard ratio is 0.99 in the first six months and 0.42 after, in a prespecified sensitivity analysis. That is the timing expected if the benefit comes from an immune response the vaccine builds against each patient's chosen neoantigens. Merck says it is now talking to regulators.

Evidence and limits: Merck and Moderna released these numbers ahead of the talk and the paper. Overall survival is immature: the release reports a 'favorable trend' and no number. The trial tests the whole product, so it cannot say how much the neoantigen selection adds over another way of choosing antigens. Grade 3-5 treatment-related events were 14.2% against 13.4%.

What happened: RFD4-Proteina, from the University of Washington Institute for Protein Design and NVIDIA (David Baker corresponding), designs proteins, DNA, RNA and enzymes in one shared latent space, using reasoning and guided search rather than a separate model per task. On AlphaFold3-refolded designs it reports a 10.6% pass rate for protein binders against BindCraft's 5.9% at 42 times the throughput, and higher rates than every method tested on small-molecule, enzyme-scaffold and DNA-binding tasks. In the lab, 158 of 188 designed TrkA binders bound below a micromole.

Why it matters: Protein design has used a different specialist tool for each job: backbones, binders, enzymes, DNA. RFD4-Proteina puts them in one model that also covers modified amino acids, sugars and nucleic acids, and still comes out ahead on all four. The part that matters is the wet lab: designs across many target classes were made and measured, including a designed enzyme that converted 79% of an intermediate in making the HIV drug lenacapavir, a de novo catalyst doing a real step of drug chemistry, further than designed enzymes had reached.

Evidence and limits: A preprint, not peer reviewed, and all the wet-lab work is the authors' own, so nothing is independent yet. In silico a 'pass' means an AlphaFold3 refold matches the target (a model checking a model), and the pass rates are low in absolute terms (3.5% for DNA binders). The binder and enzyme successes are counts from the authors' own assays, not an outside lab's, and the enzyme result is a single proof-of-concept.

By Nathaniel Corley, Kieran Didi, David Baker

What happened: A. Ali Heydari, Daniel McDuff, Xuhai Orson Xu and colleagues at Google Research built an orchestrator over three Gemini 2 agents: one writes and runs analysis code on Fitbit and blood-test data, one checks health knowledge against sources, one coaches. The data came from the WEAR-ME study of 1,165 people. Across 11 tasks and 1,350 hours of rating, 45 users put it first 46.7% of the time; three health experts scored its overall quality 72.8%, against 64.3% for the three agents answering separately and 54.4% for one agent.

Why it matters: Questions about your own wearable data need code and statistics, while coaching needs a different way of talking, and the authors found one model struggles to balance both. Splitting the work let each part be tested on its own: the data agent's analysis plans scored 75.6% against 53.7% for plain Gemini, and its code passed 75.5% of expert unit tests on the first try against 58.4%. The framework and its benchmark are open under Apache 2.0, so other groups can swap in their own models.

Evidence and limits: Everything was rated in conversation, not measured in health: the live users played study personas instead of asking about their own data, and no one tested whether behaviour changed. The data are Fitbit only, some scoring used model raters, and a response took 205 seconds and 6.5 model calls, against 36 seconds for one agent. The authors call it a research prototype, not a diagnostic tool.

By A. Ali Heydari, Xin Liu, Daniel McDuff, Xuhai Orson Xu

What happened: Mirendil reports that one researcher built Mirendil-Base from an empty repository: agents wrote over 99% of the code, ran the experiments and diagnosed failures, while the researcher chose what to try and what to trust. It learned only from public code, web pages and datasets, with no distillation, and the cost covers all compute, the agents' tokens included. On seven code repositories published after training it scored 0.222 bits per byte (lower is better), behind Kimi-K3's 0.193 and ahead of GLM-5.3, DeepSeek-V4-Pro-Base and Nemotron-550B-Base.

Why it matters: Pretraining a competitive base model has taken a team of specialists in architecture, data and chips. If one person directing agents can do it, the scarce inputs become compute and judgment, and a group whose expertise is in its own science, such as a robotics lab or a hospital, could train a model on its own data. It is the first result from a company that raised on the pitch that labs using AI in biology or chemistry should not have to become AI labs.

Evidence and limits: Every number is Mirendil's own, in a blog post with no paper, no weights and no named researcher. The only score is how well the model predicts unseen code, not whether it solves coding tasks, and Mirendil notes that Kimi-K3 and GLM-5.3 were scored after post-training, which usually raises this loss slightly. The two larger models are still in training.

What happened: Jeonghyeon Kim and Philip A. Romero connected GPT-5.6 Terra to a standard Opentrons OT-2 pipetting robot and a webcam through PEARL, five model roles sharing one record of the experiment. Given only a goal, it refused an impossible instruction in all ten runs, read from the colours that a human had turned the plate 180 degrees mid-run, and in seven of ten diluted dyes to see their colour first. Asked to characterise an enzyme assay, it set concentrations and timings, discarded a spiked well, and repeated runs missing early readings.

Why it matters: Lab automation runs the procedure it was given, and a moved plate or a saturated reading breaks it unless someone wrote a check for that case first. The paper's own comparison locates the behaviour: in a plain tool loop none of the 80 execution runs passed, one agent keeping a persistent record of the experiment passed 60%, and the five-role version passed all 80. So the judgment is mostly the general model's and the scaffolding buys reliability, which is what would let a lab with an ordinary liquid handler try this rather than build a platform.

Evidence and limits: Eight scenarios on one bench, run by the authors: colour mixing and one horseradish peroxidase assay, whose kinetic constants matched their own human-run reference and previously reported values, not an outside lab's. The scoring of planning, adaptation and execution is theirs, on their own rubric, and every perturbation was one they introduced, so nothing tests what a working lab meets unplanned. Colour was read off a webcam calibrated against a plate reader. It is a preprint.

By Jeonghyeon Kim, Philip A. Romero

What happened: AlphaVariant pairs a sequence generator trained on related natural proteins with activity predictors retrained after each round of results. On savinase, a detergent protease, two rounds produced libraries in which 75.4% and then 83.8% of the variants tested beat wild type, as the positions it could change went from 25 to 67; its best variant was 7.1 times more active than wild type on a synthetic substrate. A third round also scored heat tolerance and patent distance, and of 32 variants built, three beat the commercial protease Uno 100 L.

Why it matters: Directed evolution gets its gains mostly one position at a time, because the combinations explode and the measured data is far too sparse to fit a model over them. Here the useful variants carry changes at eight to sixteen positions at once, and the final round needed only 32 measurements, which is the scale at which a design method is worth a company's screening budget rather than a benchmark table. Putting patent distance in the reward is the unusual part: freedom to sell was optimised alongside performance, and it is a constraint any enzyme has to satisfy.

Evidence and limits: Every assay and the wash test were run by the authors with the industrial partner Bestzyme, which has filed a patent application on the variants; no outside lab has tested them. The wash test is a Chinese standard method on one soiled-cloth type, not a product trial. The 7.1-fold figure is kinetic fitting on a peptide substrate, and round three used casein. On the six public landscapes the clear wins were the three scored by a surrogate. Preprint, CC BY-NC-ND.

By Xiaopeng Xu, Jie Wang, Xin Gao

What happened: Bo Wang, Ci Chu and colleagues describe X-Scientist on the company's blog: a lead agent plans and checks the work of worker agents that call bioinformatics tools and Xaira's own models. It nominated 381 genetic perturbations to undo what TGF-beta does to HepG2 liver cells; the company also tested 321 its own scientists picked and 433 drawn at random, measured all of them by Perturb-seq, and counted a hit at three standard deviations back toward untreated cells. Nominating took 20 minutes against more than three hours by hand.

Why it matters: TGF-beta drives scarring in chronic liver disease, and which genes to perturb against it is the kind of choice a biologist spends a morning on. The measurement here is of the model's picks, not of the model's own score: every nomination was made, perturbed and read out in cells. Three of its hits outside the known TGF-beta pathway beat the median of the ones inside it, and none were in the scientists' list, which is what the loop is for, since a list of known pathway genes is one a scientist already has.

Evidence and limits: A company blog post, no paper and no peer review, and Xaira both chose and scored the comparison. One cell line, one exposure out of the twelve it ran, and the 7.1% against 6.9% gap is well inside what 381 and 321 perturbations can resolve. The benchmark scores beside it compare harnesses on the same models, not biology. Nothing has gone back into the models yet.

By Bo Wang, Ci Chu

What happened: AI Proteins will design and optimise miniproteins from scratch against several targets Astellas picks. Astellas, through its affiliate Astellas Innovation Management, takes an exclusive worldwide licence to any candidate and runs development, regulatory work and sales. Upfront and milestone payments come to more than $400M, plus royalties. Chris Bahl, the Boston company's founder and CEO, and Morten Sogaard of the Astellas Innovation Lab spoke for the deal.

Why it matters: This is AI Proteins' second large-pharma deal, after Bristol Myers Squibb's in late 2024, also valued at up to $400M. Miniproteins are small proteins built from scratch rather than taken from nature. Bristol Myers Squibb took an option on a licence; Astellas takes the exclusive licence outright, so this time the designs are bought as future drugs from the start.

Evidence and limits: The release does not break out the upfront payment or say how many targets are involved, and no designed molecule from the deal is shown. One trade report described the $400M as upfront; the release counts it as upfront and milestones together.

What happened: Fengyuan Dai, Fajie Yuan and colleagues at Westlake University trained a 16-billion-parameter model on 1.7 billion protein-text pairs: a description of the function wanted goes in, a sequence comes out, with less hand-written structural specification. Four tasks were run, a fluorescent protein, a PET hydrolase, an alcohol dehydrogenase and a metabolic H-protein, and each gave the intended function, with catalysis measured for both enzymes. In a reconstituted CO2-fixing reaction a designed H-protein raised product yield 1.7-fold over the natural E. coli one.

Why it matters: Protein design normally starts from a natural protein to optimise, or asks the designer to restate the goal as geometry and sequence rules a model can take. Here the goal is a sentence, and what came out of it catalysed reactions rather than only folding as predicted. The H-protein is the sharper part: a designed carrier protein beat the natural one in a reconstituted CO2-fixation reaction, with no rounds of experimental optimisation in between.

Evidence and limits: The per-task numbers are in the extended data and they are small: 94 PET hydrolase designs assayed out of 2,416 filtered gave one hit, 8 alcohol dehydrogenases tested gave 5, and 20 H-proteins picked from about 1,000 gave 5 active. All the wet-lab work is the authors' own. The inference code is Apache 2.0 and the weights repository states no licence; the full training corpus was withheld for biosecurity and given only to the reviewers.

By Fengyuan Dai, Fajie Yuan

What happened: Fajie Yuan's group at Westlake University, with HKUST (Guangzhou), the Shenzhen Institute of Synthetic Biology and Zelixir, trained Pinal, a 16-billion-parameter model, on 1.7 billion machine-written pairs of a protein and a description. It drafts a structure from the words, then a sequence to fit it. Designs for a fluorescent protein, a PET-degrading enzyme, an alcohol dehydrogenase and an H-protein each showed the function asked for; in a CO2-fixing pathway the designed H-protein gave 1.7 times the product of E. coli's own, with no lab optimisation.

Why it matters: Design models typically start from a natural protein to improve, or need a person to turn the goal into shape and sequence constraints first. Pinal takes the goal as a sentence, which is a different interface: the biologist says what the protein should do and gets candidates to test. The weights are on Hugging Face and the code is Apache-2.0, so the claim can be tried by anyone.

Evidence and limits: Each hit came out of a funnel the headline hides: about 151 designs assayed across the four functions, out of thousands generated, with the PET-degrading enzyme leaving one standout among 94 tested. The measurements are the authors' own, and the training corpus is withheld over dual-use risk. Fajie Yuan told Science and Technology Daily that language-driven protein design is still at the proof-of-concept stage, with finer instructions, such as setting a fluorescent protein's emission band, open for the field.

By Fengyuan Dai, Shiyang You, Tong Si, Fajie Yuan

What happened: OpenAI says it has acquired Orchestra Bio, a platform for coordinating drug development, and is bringing its team and technology into Rosalind. Its own example: a failed manufacturing batch can delay the start of a clinical trial, and Orchestra's agents work out which activities and timelines that hits, gather the relevant information and compare ways to respond, after which scientists and programme leaders decide. The same page names Novo Nordisk and Moderna as Rosalind partners.

Why it matters: Rosalind has been a model and a workbench; this adds the layer that tracks what a programme owes and when. OpenAI puts the case in its own numbers: roughly 10 to 15 years on average from target discovery to approval in the United States, with teams coordinating across internal departments and outside partners on disconnected systems. So what is being sold here is coordination rather than prediction, and the division of labour is stated outright: agents monitor and track, people decide.

Evidence and limits: No price, and OpenAI's page names nobody from Orchestra. The reported funding, about $20 million in total, reaches here through Decrypt citing Caplight rather than from the company, and the seed round's size is not given. Neither company says how many customers Orchestra has, what happens to existing contracts, or when the agents reach Rosalind. Orchestra Bio is not Orchestra BioMed, the unrelated Nasdaq device maker.

What happened: Aniketh Iyengar, Benjamin Siranosian and colleagues report it on the company's blog. Omnii-1 rewrote the DNA for gp17, the tail-fibre protein the T7 phage uses to recognise E. coli. At 11 to 15 amino-acid changes from the natural protein, 69.9% of its designs were recovered after assembly against 4.8% of random controls, and 2,161 of 6,694 variants with measurable activity were enriched above the natural phage. A cocktail containing its designs cleared one patient's resistant bacteria in a dish, then was given to that patient.

Why it matters: A model's design has gone into a person's treatment, not only into a dish: the company says this is the first clinical use of AI-generated phages, under an authorised, physician-led compassionate-use pathway. What the model wrote is one region of a known T7 genome, not a genome. It also ranks which designs to test against a given bacterial strain (on the hardest of three library strains, 24% of its top 100 picks beat the activity threshold against 4.5% of the library), and that ranking is the step a therapy matched to one patient has to get right.

Evidence and limits: A company blog post, no paper and no peer review, and every measurement is the company's own. One patient: the company discloses no clinical course and no outcome, citing privacy, and says a single patient cannot establish safety or efficacy, so nothing here tests whether the treatment worked. "World's first" is the company's own claim. Recovery is a sequencing proxy rather than a plaque count, and activity is enrichment measured against the natural phage.

By Aniketh Iyengar, Benjamin Siranosian, Arpan Sarkar, Avery Noonan

What happened: The team chose five toxins; from one prompt and four follow-ups, an internal research model then worked mostly on its own for 64 hours, picking the sites to aim at and generating 318,484 candidates, 211 of which were made. Adaptyv Bio tested all of them for binding, Twist Bioscience 210 against four toxins. Seventeen met Anthropic's criteria, 15 of them at both labs. The tightest, against the Mexican toxin Cn2, was single-digit nanomolar. All 211 and their data are released.

Why it matters: This is the company's earlier binder work pushed at a target nobody has money to work on (antivenom for a neglected disease), and the loop is what to look at: the model set its own quality thresholds, decided how much to lean on each design tool the prompt offered, and sent sequences to a vendor, with people steering four times. Among designs aimed at a single toxin, 11 of 60 bound, which is what an ordinary protein-design campaign gets, so the claim is about who ran the campaign, not about a better method.

Evidence and limits: Binding is all that was measured, against toxin proteins grown in the lab, not taken from venom; nothing was tested in cells or in an animal, and Anthropic says it has not made an antivenom. None of the 151 designs aimed at more than one toxin met the bar against more than one. The criteria were set after the data came in, and no design baseline was run, so the model's own contribution cannot be separated from its tools'.

By Nathaniel Horwitz, Amir Shanehsazzadeh

What happened: Yazhuo Liu, Ting Zhu and colleagues at Georgia Tech, the University of Osaka and USC build GBClaw: an orchestrator and five agents, routed to two GPT-5.6 models, that set up atomistic shear simulations in LAMMPS and analyse them. Across 14 asymmetric tilt boundaries in nickel the strength at which sliding starts ran from 14 megapascals to 9.3 gigapascals. Four were rebuilt with arrays of secondary misfit dislocations, the defects a real boundary has: one fell from 1,074 to 530 MPa, two by four orders of magnitude, one rose.

Why it matters: Most interfaces inside a real metal are these mismatched boundaries, and they stay understudied because their structure is hard to pin down in both experiment and simulation. What the sweep shows is about the models, not the agent: if the predicted strength depends this much on whether the boundary's own defect structure sits in the cell, then screening boundaries on simplified cells (with a first-principles or machine-learned potential, at real cost) is reading the cell as much as the material.

Evidence and limits: All of it is simulation with one empirical nickel potential: nothing was made or measured, and the authors call their numbers athermal instability thresholds for the cells they modelled, not universal upper bounds. The cells are small and periodic, which suppresses the long-range defect interactions a wide boundary has, and the energy paths carry no temperature. The {111}/{110} anomaly was traced only after a researcher directed the follow-up runs.

By Yazhuo Liu, Ting Zhu

What happened: Miguel Marques and Silvana Botti's group at Ruhr University Bochum ranked about 2.8 million near-stable compounds in the Alexandria database with two machine-learning models and a fitted formula, kept about 1,200, added about 12,800 compounds already computed, and narrowed the combined set to 52 for full electron-phonon transport calculations. Tested on elemental metals, those calculations land mostly within 30% of measured values. The best compounds, Be2IrPt, H3NaNi, LuIr and LiBePt2, come out at about 34 to 38 million S/m, near aluminium's measured 36.59.

Why it matters: Chip wiring and power lines need metals that conduct well, and conductivity is a property materials databases rarely compute because the full calculation is expensive. This shows a workable route: rank millions cheaply, then spend the full calculation on a few dozen. It also explains why silver, copper and gold stay ahead, and points to a mechanism: electrons from light elements can push a compound's high-scattering d-states below the Fermi level, as in LiBePt2.

Evidence and limits: The match with experiment is for elemental metals, not the new compounds, none of which was made or measured here. Even among elements, calcium comes out at 7.98 against a measured 28.99 million S/m. Spin-orbit coupling was left out; adding it cut Be2IrPt and LiBePt2 by 8% and 9%. Several leading candidates contain platinum, iridium or rhodium.

By Thalis H. B. da Silva, Silvana Botti, Miguel A. L. Marques

What happened: A Sydney, Columbia, BAAI and Tsinghua team re-scored 95 predictors over 217 ProteinGym assays and 2.47 million variants by what each would find under a matched budget, on two objectives: finding top variants, and ranking the landscape. Across 20 scores fitted to 20 measurements of the target assay, prior ranking ability explained under a fifth of the differences in what they recovered (r = 0.44). A plain additive model from measured single mutants then overtook them: at 50 to 100 measured singles for top variants, around 500 for ranking.

Why it matters: This changes what a protein-model leaderboard is for. A campaign's question is which variants to measure next, and the paper shows the quantity that answers it is not the one benchmarks report: a score that ranks a landscape well need not direct a budget well. The practical reading is that a model prior is worth most in the first few dozen measurements, after which the lab's own data wins, and that closed-loop design should be scored by what gets discovered at a given budget, not by correlation on a fixed dataset alone.

Evidence and limits: All retrospective, re-scoring measurements that already exist, with no new experiment and no closed loop run. Substitution landscapes only; the authors say insertions, deletions and de novo design would need their own controls and stated measurement costs. The crossings are additivity predicting double mutants from measured singles; on variants with three or more substitutions the models led instead. They come from deep mutational scans, where every variant is already measured, which is not a lab's candidate pool. A preprint.

By Wenze Ding, Haipeng Gong, Zhiyong Wang

What happened: Matthew Carter and colleagues post the numbers on the company's blog. On AlphaGenome's own panel of 21,948 test cases, each pairing a DNA change with a gene and a tissue, half of them changes statistics pinned as the cause and half it ruled out, Omnii-1 scores 0.810 against Borzoi's 0.705 and the 0.713 AlphaGenome's paper reports. On a splicing panel of 14,458 cases it scores 0.814 against 0.721 for Pangolin. It also predicts methylation, a chemical tag on DNA, one site at a time, agreeing with measurements at 0.879.

Why it matters: Most DNA changes tied to disease sit outside the stretches that code for proteins, and working out which of them does anything is the bottleneck. The earlier models read DNA alone. This one also reads lab measurements of how the DNA is packaged and used in a given tissue, and can take those measurements as an input rather than only predict them. Its lead over Borzoi grows the further the change sits from the gene, from 0.05 within 500 bases to 0.19 beyond 50,000.

Evidence and limits: Every score is the company's own, in a blog post with no paper and no peer review. It says it cannot run AlphaGenome itself, so those numbers are read out of the published paper rather than measured side by side, and AlphaGenome's higher supervised score on the same panel, 0.799, is not the one compared against. The labels are statistical inferences from population data, not variants tested in an experiment, and the model is behind a request form.

By Matthew Carter, Armin Thomas

What happened: Max Highsmith and colleagues report it on the company's blog. The campaign began with 1,620 designs from earlier pipelines, 150 of them with measured binding. Since mid-September, Omnii-1 rewrote positions in the best of those, ranked its own proposals, and Aptamino made and measured a selected set, three times over. The designs push out the measured trade-off between how tightly a single-domain antibody binds and how much of it a cell makes; two from the third round bind below the test's floor.

Why it matters: This is a loop where the model's own ranking decides what gets made, and each round's measurements go back into it. Antibody sequences were not a domain of this genome model's original pretraining; they came in afterwards, as public repertoires and the partner's own measured designs. What is being tested is whether a general biological model can be turned to one partner's narrow design problem. Ranked on the same 327 measured designs, what the model has learned predicts expression and potency a little better than protein-specific models, with a plain one-hot encoding of the sequence close behind on potency.

Evidence and limits: A company blog post, no paper and no peer review, and both sides report their own work. One target, single-domain antibodies, three rounds, with the target's identity and some measured properties held back by the partner. Two designs beating the test's floor is a limit of the test, not a measured strength; the antibody post gives no number for that floor, and 0.2 nM comes from the launch post. The gains over the other models sit inside overlapping confidence intervals.

By Max Highsmith, Michael Poli

What happened: Armin Thomas, Gautam Machiraju, Benjamin Siranosian and Christine Parthemore report it on the company's blog. Livermore measured how 609 natural proteins split their binding across the 15 rare earth metals, on a plate assay it built this year. Given two of the metals, Omnii-1 ranked proteins it had not seen by which one they favour, at a median rank agreement of 0.340 over all 105 pairs, against 0.318 for the strongest public model, ESM-C 6B, and 0.254 for the best of three methods that go by sequence similarity.

Why it matters: Telling these metals apart is a refining problem: China refined 91% of the world's supply in 2024 on the International Energy Agency's count, and the elements differ so little in size, 0.013 angstroms between neighbours, that the standard route needs harsh acids and a lot of energy. Proteins can distinguish them, and the question is whether a model can say which protein will, before anyone makes it. This model had no rare earth binding data in its training, and the lab is now testing the candidates the model generated and ranked.

Evidence and limits: Prediction only: the proteins the model designed are at Livermore awaiting testing, and no result from them is reported. The company's post, its own evaluation, no paper and no peer review. On the 17 pairs closest in size, the hardest cases, the company puts the gap to ESM-3 at 0.006 against a paired error of 0.005 and declines to rank the two. AlphaFold2 scored 0.191 here, below the simplest sequence-similarity baseline, 0.198.

By Armin Thomas, Gautam Machiraju, Benjamin Siranosian, Christine Parthemore

What happened: Matt Turk, James Golden and colleagues at Protege DataLab and Heimdall Bio ran 4,249 rollouts of four frontier models, plus 4,486 trials across 26 models. Claude Opus 5 (0.816), GPT-5.6-sol (0.812) and Grok 4.5 (0.785) are not separated by the intervals; Gemini 3.1 Pro Preview scores 0.707 and every gap to it excludes zero. Opus 5 averages 0.907 on metadata lookup and 0.716 on numeric calculation. Numeric tasks are 28 of the 131 but six of the twelve tasks all four models fail.

Why it matters: A researcher picking an agent to measure distances and angles in a structure needs the numeric score, not the overall one, and for Opus 5 the two differ by 0.19. The paper also shows how much of a leaderboard is the setup around the model: on the 99 tasks with eight full runs, one run per task instead of eight puts a different model first in 23.9% of resampled task sets, and swapping each provider's own command-line agent for the reference harness moves scores by -0.071 to +0.086.

Evidence and limits: The four experiments use different task subsets, so model, harness, access and task mix cannot be ranked against one another, and the harness comparison can only detect differences of about 0.12 to 0.20. A task all four fail is not shown to be unsolvable. The 131 tasks lean to biophysics, and the scores describe those model endpoints in the collection windows the appendix gives. A preprint, not peer reviewed.

By Matt Turk, Brandon L. Neel, Diana M. Lopez, James B. Golden

What happened: Manuel Gallone, Shyam Ravidath and Srihith Madireddy built an out-of-distribution harness (two datasets, six dataset-split combinations, scVI, CPA and a Ridge baseline, three uncertainty methods), then retrofitted null baselines it had never included. The harness fed each test cell to the model and scored it on reproducing that same cell, so an identity predictor reaches a perturbation-effect correlation of 1.0 everywhere. Against a constant train-mean predictor, scVI wins on 99.7% of Norman cells and 0.2% of Replogle cells.

Why it matters: Perturbation prediction is one of the directions this record follows most closely, and the audit says metric names there can outrun what is measured. The six failure modes are mechanical rather than statistical (scoring reconstruction as prediction, holding out sequencing batches instead of perturbations, silently swapping an unseen perturbation for a control), and each comes with a diagnostic that needs no retraining, so another harness can be checked largely from what it already stored. The one model never handed the target cell, the linear baseline, is the only one that beats its null on a genuine counterfactual.

Evidence and limits: It is the authors' own harness, not a published benchmark; PerturBench is cited but never audited. The audit needed no retraining, though models were re-run counterfactually, and everything is computed on two datasets with no new experiment. One result survives: split-conformal coverage holds at 0.900 in distribution and falls to 0.847 across cell types, while on Norman that nominal 0.900 covers 0.271 to 0.342 of differentially expressed genes.

By Manuel Gallone, Shyam Ravidath, Srihith Madireddy

What happened: Joseph Hart, Samuel G. H. Brookes, Fabian L. Thiemann and Christoph Schran screen six models on density-functional energies, forces and binding curves, then put four through melt-quench-anneal dynamics at seven densities, five runs each. Every pure-carbon run finishes. Adding oxygen and hydrogen separates them: MACE-MH-1 completes all 35 runs, ORB-v3 33, MACE-MP-0b3 25 and MACE-OMAT-0 18. UMA never reached this stage (its carbon-dimer curve has two spurious minima) despite low errors on flat-carbon environments.

Why it matters: A foundation potential is sold on transferring to systems it never trained on, and the usual evidence is a table of energy and force errors. Here those errors do not say which models break: UMA scores well on flat carbon, then is thrown out for a dimer curve no potential should have, while models sharing a training set share ring statistics across architectures. All four that ran reproduce the main experimental peaks for nanoporous carbon, so the question is where they stop, not whether they work.

Evidence and limits: One element family and one workflow. Stability is scored in 216-atom cells; the structures compared with experiment are larger, at one density, and matched to published measurements of different samples. The first author co-authored both the model that comes out best and the scoring framework the error table uses, and the paper declares no competing interests. Two of the six never reached dynamics, one on cost alone. It is a workshop paper.

By Joseph Hart, Samuel G. H. Brookes, Fabian L. Thiemann, Christoph Schran

What happened: METiS announced a strategic collaboration with Lilly on 19 October to research lipid nanoparticle delivery that reaches one tissue rather than everywhere. METiS runs its own AiLNP platform, which designs the ionizable lipid inside the particle, and Lilly provides its drug discovery, development and commercialisation expertise. METiS takes an upfront payment of over $10M, is eligible for up to about $1.1B in development, regulatory and commercial milestones, and single-digit tiered royalties on net sales.

Why it matters: The record holds METiS's Hong Kong listing, its mouse data for a liver-cancer mRNA drug and a Phase 3 formulation result. This deal is about the platform rather than one drug asset: Lilly is paying for the design step, which is where METiS says its AI sits. Where a nanoparticle ends up in the body decides what an mRNA or gene therapy can treat, and it is still mostly found by trial and error.

Evidence and limits: A deal, not a result: no target, no programme count, no timetable, and nothing public about how well AiLNP picks lipids. All the money beyond the upfront is contingent on programmes advancing, and the upfront is stated only as a range.

What happened: A Zhejiang University team ran DFT calculations, about a million CPU core-hours, on 45,909 frameworks from six databases, compressed the resulting fields about 26-fold and turned them into point clouds. Models reading the fields beat a structure-only model on 12 of 13 properties, all the adsorption and separation ones among them; the structure model kept band gap. Combining the two did better still on every property. Electron density predicted from structure is off by 0.51% on average, and starting DFT from it cut run time by 13.59%.

Why it matters: MOF models mostly learn from atom positions, while gas adsorption depends on how a guest molecule feels the framework's charge. Aligned electronic fields at this scale were scarce because they are costly to compute and store; the authors say the dataset will be released when the paper is published. The results say the fields carry information structure-only models were not using, most of all for adsorption and separation.

Evidence and limits: All computed, by the authors. The structure baseline is one model, CGCNN from 2018, so the comparison with structure rests on it. The electrostatic potential is reconstructed far worse than the density, 8.42% off and worse in large cells. The 13.59% saving leaves out the time spent predicting the density, and is an average: some frameworks ran slower.

By Zhaolin Hu

What happened: James Kirk, Lee von Kraus and colleagues trained a Transformer, without labels, on over 4 million hours of recordings from electrodes in more than 1,400 plants of 100 species, in labs, greenhouses, orchards and forests. A second part, trained on over 41,000 handheld leaf readings, estimates seven stress measures such as how efficiently leaves photosynthesise and how warm they run. On held-out plants of six species under drought, its estimates moved with the readings at correlations of 0.27 to 0.71.

Why it matters: Earlier models of plant electrical signals were each trained for one task, usually on one species. One model that carries across species would let a grower read a crop nobody has labelled. On species left out of training the estimates still moved with the readings, less closely, and on plants stressed by salt or a herbicide, which the model never saw labelled, they shifted the way the literature expects.

Evidence and limits: The data, sensors and evaluation are all the company's own, in a preprint. The correlations follow one plant over time; about half the error is a fixed offset per sensor, and on the error measure, three of the seven estimates did worse than guessing each species' average. One of the six densely labelled species read at or below zero, and nothing yet shows a grower acting on it.

By James Kirk, Piotr Bednarski, Filip Twardy, Justin Mast, Lee von Kraus

What happened: The two endpoints return ranked web results with a relevance score and an authority tier, and a page's content as Markdown. DeepSeek says its in-house engine has long served the search in its web app and API, handles hundreds of millions of requests a day and indexes hundreds of billions of pages, with filters for SEO spam, GEO manipulation and low-quality AI-generated text. Calls made through the server-side tools in DeepSeek Harness, Claude Code and Codex stay free until 31 October.

Why it matters: A literature agent needs a search backend it can call by program, and almost all of them sit on Bing's or Google's index. This is one of the few that does not, from a lab whose models many Chinese AI4S groups already run, priced per call rather than by tier. Whether its index covers journals, preprint servers and Chinese-language sources well enough for science is for anyone building on it to test.

Evidence and limits: The request and index figures are DeepSeek's own and unaudited; the English release note gives the daily requests and the Chinese one the index size. No coverage or freshness comparison against other search engines was published, and the endpoints were not called here.

What happened: Zachary Cutts, Pooja Rao, Annalisa Pawlosky and colleagues gave Gemini-based agents literature search, folding tools and a Python environment, and had them design 1,000 single-stranded DNA aptamers per round against two proteins. Each pool was synthesised, pulled down on beads and sequenced, and the agents read the counts before designing the next. Corrected for bead background, hit rates went 0% to 26% to 42% on alpha-thrombin, or 37% counting only designs free of published motifs, and 10% to 4% to 34% on myoglobin, at about $3 a candidate.

Why it matters: Earlier studies of reasoning agents in molecular design tested their designs once, at the end. Here the measurements came back three times, and what the agents did with them is on the record: after a counter-screen showed that 97% of the cross-reactive sequences were G-quadruplexes, which stick to charged surfaces whatever the target, they moved the myoglobin designs to pseudoknots and hairpins, and specificity there went from 19% back to 72%. The authors picked aptamers because the designed sequence is the molecule, so one cycle is 10 days instead of the 3-6 weeks a protein needs.

Evidence and limits: Binding is measured in the authors' own pooled assay. Single-clone titrations covered eight thrombin designs only, did not reach saturation, and the authors call those affinities preliminary: nanomolar for reworked literature motifs, above a micromolar for de novo ones. Round 1's 0% on thrombin was a construct artifact the agents then fixed, the harness changed after it, and the authors say control arms with no feedback would be needed to credit the loop itself. A preprint, not peer reviewed.

By Zachary Cutts, Pooja Rao, Annalisa Pawlosky

What happened: Tatsuki Hori, Yoshitaka Moriwaki and Ryuichiro Ishitani at Institute of Science Tokyo and the University of Tokyo release RGI-Toolkit, which corrects the denoiser output at each reverse-diffusion step with a differentiable restraint loss, as a backend-agnostic library wrapping six predictors: Boltz-2, Protenix-v2, AlphaFold3, Chai-1, OpenFold3 and ESMFold2. On 209 protein-ligand complexes, conformer restraints lifted chirality agreement from about 50% to 100% and cis/trans from 70-80% to 100% for all six, with lower bond-length and bond-angle errors. No retraining. MIT-licensed.

Why it matters: Chemically invalid ligand geometry is a known weak spot of AlphaFold3 and its kin, and this fixes it from outside the model, at inference, without retraining it or changing a weight. It does not touch where the ligand sits: the authors report pose accuracy is neither helped nor hurt, so the gain is a clean stereochemistry patch, not a better binder prediction. Geometric and custom restraints can also push sampling toward a chosen conformational state.

Evidence and limits: The chirality and cis/trans figures are computed agreement rates against each ligand's ideal conformer, on 209 complexes with one seed each, not a physical measurement. The restraints only work as well as the reference conformer: a bad RDKit embedding misguides them. The two earlier RGI papers this builds on were in Boltz; the new work is extending that to six predictors and packaging it. A preprint, not peer reviewed.

By Tatsuki Hori, Yoshitaka Moriwaki, Ryuichiro Ishitani

What happened: Scripps Research, OpenAI and PerturbAI gave one agent to each of 1,946 perturbations in an in vivo whole-brain CRISPR Perturb-seq atlas, with the source manuscript withheld and published after the model's stated knowledge cutoff. Of 55 findings derivable from the expression data alone, the agents matched or refined 48. They adjudicated 1,519 high-confidence findings against the literature, reading 39,185 papers and keeping 16,445 citations. The run took 32 minutes.

Why it matters: The bet is coverage through many parallel agents rather than one stronger agent, and the paper measures it directly: at 893 perturbations a single agent cited 11 references while the distributed run kept 711 times more. Blinded experts preferred the distributed readings. This is a throughput claim about interpretation, the step that now limits what a large screen yields, not a claim that the agents found new biology.

Evidence and limits: All computed, on one atlas the same group produced, and every corresponding author has a stake: one at OpenAI, whose model this evaluates, two who co-founded PerturbAI, which hosts the data. Recovery is scored by the source study's own authors, 14 of 71 findings uncovered. Held-out predictions reach 0.705 balanced accuracy: 82% of the silent perturbations called, only 59% with a real response. The expert preference ranks readings, not their correctness. A preprint, not peer reviewed.

By Alexander Sun, Xin Jin

What happened: Andi Gu, an Anthropic STEM Fellow, with Alek Kemeny of Anthropic and Harvard's Susanne Yelin and Mikhail Lukin, had Claude Mythos 5 write circuits in Python, each checked and scored by an evaluator. In six tasks the circuits cut both Toffoli and qubit counts against the best published design; a seventh had no published reversible design to beat. In the eighth, a new reversible modular inversion, its key step formalised in Lean, gives one design 17 times fewer gates than the smallest previous one, for 12% more qubits.

Why it matters: Most of the fall in what useful quantum chemistry is expected to cost has come from redesigning circuits, not hardware: the FeMoco estimate dropped about 150,000-fold over eight years of human work. A loop where the model writes circuits as code and a program checks every one speeds that redesign up: Anthropic sets the elliptic-curve design, from a single 53-hour run, against eight years of field-wide work. The same loop cut the costliest step of the DQI optimisation algorithm, its decoder, about 12-fold.

Evidence and limits: All of it is computed: the circuits have not run on any quantum computer, the counts come from the team's own evaluator, and the arXiv preprint is not peer reviewed. People wrote the briefs and at times told Claude to search more broadly.

By Andi Gu, Alek H. Kemeny, Susanne F. Yelin, Mikhail D. Lukin

What happened: A model could be blocked only on a foreseeable, imminent risk of over $1 billion in damages or of killing or seriously injuring more than 50 people, from a biological weapon, a model evading its developer's controls, or a cyberattack. Commerce would ask a federal judge in Washington to order it; no other agency could. Evaluators would register with Commerce, may be lab-paid if the fee does not turn on their findings, and must report an imminent risk within 72 hours. Some state AI laws would be preempted.

Why it matters: A frontier lab has written the text that would govern it. Politico notes its threshold language mirrors California's SB 53, and Anthropic calls the bill its Advanced AI Framework, pushed since June. For science the category that matters is biological weapons, one of three grounds on which a model could be stopped: an evaluator's report to Commerce would be the first step toward a court order there.

Evidence and limits: The draft is confidential and Politico did not publish it, so every provision here is Politico's account of a text it obtained; three people with knowledge are its sources for the pitch itself. The article does not name which lawmakers received it. An unnamed Anthropic spokesperson endorsed the bill as the company's framework without publishing the text.

What happened: Google DeepMind fitted its own capability index, GCI v1, to 123 models on 92 benchmarks. Public Gemini models gain about 0.1 points a month: 0.091 before 1 April 2026, 0.118 after, a difference that is not statistically significant. Most production code is now over 75% AI-written, yet fully delegated pull requests rose only from 7.5% in April to 11.9% in September; about 88% still need multi-turn human steering or human-written code.

Why it matters: Whether AI is speeding up its own development now has a frontier lab's internal measurements behind one answer, set against the September working paper co-signed by Geoffrey Hinton, Yoshua Bengio and OpenAI's chief scientist, which estimated that fully automated research could make progress tenfold faster. A survey of 12 senior researchers puts automation highest on well-specified, verifiable tasks and lowest on open-ended scientific ideas and research taste. Argon sits above the trend, which the report suspects is due to more compute and organisational investment.

Evidence and limits: The index is DeepMind's own and built on benchmarks: a steady slope means measured capability grew steadily, not that no research step got faster, as the report itself notes. The post-April slope rests on few models (0.053 to 0.171 at 95%), so it cannot rule out a speed-up either. The survey and the agent-use sample, graded by Gemini Flash 3.8, are internal, and the finding supports the report's own conclusion that the risk is low.

What happened: Vinay Saji Mathew and colleagues at Penn State and the University at Buffalo distilled the diffusion stage of AlphaFold 3 and Boltz-2, the part that turns a predicted layout into atoms, into an eight-step student. On about 2,300 Boltz-2 benchmark targets, the AlphaFold 3 student scores 0.8635 complex lDDT against its teacher's 0.8615, and predicted ligands passing PoseBusters' validity checks fall from 45.0% to 43.0%. For Boltz-2 the cost is larger: ligand validity falls from 60.9% to 52.0%, with structures carrying any geometry violation rising from 30.3% to 41.9%.

Why it matters: Protein design and screening pipelines call a structure predictor tens of thousands of times, so the sampling stage's cost adds up. The paper also argues that speed-up claims have been scored too loosely: earlier few-step samplers were judged on similarity scores with the chemistry checks switched off, which stay high while bonds, angles and clashes go wrong. Blitz reports both, under one protocol for every method, and shows a one-step sampler losing about 32 points of lDDT once the checks are on.

Evidence and limits: Computed, by the authors, on public benchmarks. Only the sampling stage gets faster: the paper says the trunk, the network that runs before sampling, is unchanged and still costly, so eight steps is not a 25-fold faster prediction. No end-to-end timing is reported.

By Vinay Saji Mathew, Soundar Kumara, Gretta Kellogg, William K. M. Lai

What happened: Jiaru Bai, Alán Aspuru-Guzik and colleagues at the University of Toronto describe agents that start from the equipment in a room, rebuild it as a simulated twin, learn missing robot skills by trial, first in simulation, and write the workflow. In the one physical test, after a human approved the plan, the system wrote a 236-command liquid-handler protocol, had the arm carry the plate to a sealer and then a plate reader, and an 8-hour cadmium sulfide quantum-dot screen found the ligand and pH range where emitting dots formed.

Why it matters: A self-driving lab today runs only the procedures it was built for, and adding a new one takes specialist engineering. If agents can build the missing pieces, what a lab can try is limited by its equipment rather than its code, and researchers set goals instead of programming robots. The authors also turn each solved skill into a script that runs without the language model, because the agent loop was too slow for the chemistry.

Evidence and limits: One physical workflow on known chemistry, run by the authors; coordination across several robots was shown in simulation. The paper gives no count of human interventions or of engineering time saved, and says people still had to settle disagreements between agents and supply physical facts the instruments could not sense. It is a preprint.

By Jiaru Bai, Yang Cao, Varinia Bernales, Alán Aspuru-Guzik

What happened: Sifted reported on 15 October that Accel is set to lead a $60M round for Revolution Labs, founded by Nando de Freitas after about a decade at DeepMind and a stint at Microsoft. Dealroom's summary of the report adds that GV and NEA are said to have joined. The company builds diffusion models, extending them from images and audio to text. In September Bloomberg had reported talks to raise at least $100M initially.

Why it matters: The September figure was a target in talks; this is the first report of a round sized and led. At $60M, the first close is smaller than the $100M+ floor reported then, though Bloomberg also said the raise would likely come in several tranches. It is one more DeepMind alumnus raising for a bet on a different kind of model than today's language models.

Evidence and limits: Media-reported and not closed: Sifted's headline says Accel is set to lead, and Sifted's text could not be read here. GV and NEA come only from Dealroom's summary. No stage, valuation or company statement; no model has been published.

What happened: insitro will use its Virtual Human platform to find disease-causing biology in both diseases and take targets AstraZeneca nominates from discovery to a development candidate. The collaboration is worldwide and exclusive, and AstraZeneca runs clinical development, regulatory work and sales. Beyond the $40M upfront, insitro can earn up to about $1.46B in discovery, development and commercial milestones, plus single-digit royalties. Daphne Koller, insitro's founder and CEO, announced the deal.

Why it matters: A large drugmaker is paying for an AI company's reading of disease biology at the target stage, the earliest and riskiest decision in drug R&D, and not only for molecule design: insitro's platform looks for what causes the disease, then takes targets AstraZeneca picks through to a drug candidate. insitro already works with Bristol Myers Squibb, Lilly and Gilead; AstraZeneca is the fourth.

Evidence and limits: The $1.46B is a ceiling that depends on milestones being met; only the $40M is certain. The release names no target, no number of programmes and no timeline, and gives no data from the platform.

What happened: The two plan a long-term partnership on foundation models for science, starting with high-energy physics. The goal is a single pretrained Large Physics Model that could reconstruct collisions, identify particles, flag unexpected signals and help evaluate future detector designs: jobs now spread over many separate tools; reconstruction alone runs through a chain of specialised algorithms. Enrica Porcari, CERN's chief information officer, and Johannes Brandstetter, Mistral's vice-president of AI for engineering, spoke for the two.

Why it matters: Particle physics has used machine learning for years, but one model per task. The plan is for a frontier model company to learn across the whole reconstruction chain, at the lab holding about an exabyte of LHC data, before the High-Luminosity LHC raises the collision count tenfold around 2030. Brandstetter co-founded Emmi AI, the physics-simulation start-up Mistral bought in May, and worked at CERN before that.

Evidence and limits: A statement of intent, not a contract: CERN's release gives no budget, timeline, compute figure or terms on who owns or can use the model. Nothing has been trained yet, and 'the first foundation model for collider physics' is the release's own description.

What happened: OpenAI's terms: up to 30 ChatGPT Business Standard seats at no charge for up to twelve months, for eligible labs at US universities, with research data on those accounts kept out of model training by default. A seat does not unlock GPT-Rosalind. That needs a separate trusted-access review of the lab head's identity, employment and authority, the research purpose, and a named institutional safety contact. Subscription seats only, so no API, and a university holding an Enterprise agreement applies through the school.

Why it matters: Until now the model went to organisations through trusted access, with Amgen, Moderna, the Allen Institute and Thermo Fisher as its named partners. This is the first way in for a university lab on its own, and it says where OpenAI expects life-science demand to come from next. Without the API it stays something a researcher types into rather than something a lab wires into its own pipeline, which is the difference between a tool and infrastructure.

Evidence and limits: What counts as an eligible lab, how long the trusted-access review takes, how many labs will be admitted and what happens to access after twelve months are all unpublished. Benchmark scores for GPT-Rosalind are circulating from a single secondary source and are not on OpenAI's page, so they are left out here.

What happened: Gideon Shaked and Siddharth Nagisetty, both at the University of Michigan and AfterQuery, build each task as a directed acyclic graph of subtasks over a real biological dataset, posed one per conversational turn and scored against an answer key by 58 graders that match entities through reference databases, not by string. All twelve models ran on one harness, two on two more. Means ran 0.43 to 0.57 with no feedback and 0.54 to 0.67 when a failing answer could be retried; the slowest agent averaged 3.6 hours a task.

Why it matters: Most bioinformatics benchmarks ask one question in one prompt, which cannot show whether an agent carries intermediate results through a real analysis. Here every combination scores lower after its first turn, and lower again on subtasks that build on its own earlier answers, with feedback winning back only part of it. That shape of failure is exactly what a single-prompt score cannot see.

Evidence and limits: Scores measure agreement with an answer key, not whether the analysis is biologically sound. The authors call the retry mode open to reward hacking by design and do not quantify how much of its gain that explains. 94 of the 97 tasks are private, so the numbers cannot be reproduced from outside, and harness effects cannot be separated from model effects. The failure labels come from Opus 5, one of the models judged. A preprint, not peer reviewed.

By Gideon Shaked, Siddharth Nagisetty

What happened: Zone II and CC.VC backed it, with angels the release places in finance and Fortune in defence and finance. Fortune reports the round is all equity and values Holyvolt at $245M, and later struck Fifth Wall from its own list of this round's investors after what it calls a disclosure error by the company; the release names it as an existing investor. The lab is Wildcat Discovery Technologies, bought in 2026: 18 years of runs, more than 500,000 cells cycled. Mathias Ingvarsson is CEO, Steven Prast, formerly of Tesla, COO.

Why it matters: Most AI-for-science companies build a model and then look for data to feed it. Holyvolt bought the lab and the 18 years of cycling data first, then trained on them, and its models now pick what the lab makes and cycles next. The timing comes from procurement: from 1 October 2027 the Pentagon cannot buy batteries from CATL, BYD and four other named Chinese makers, and Holyvolt plans to sell its DRX cathode into drones first.

Evidence and limits: Nothing here is a result. The ten-times figure comes from one customer case study, and the hundred-times figure for the closed loop is the company's own. DRX data has been shown at scientific conferences, the International Battery Conference in March 2026 among them, and the suppliers are undisclosed. The $245M valuation rests on one person Fortune does not name.

What happened: Ibuki Okuda, Izumi Takahara and Teruyasu Mizoguchi extend Microsoft's MatterGen so one network denoises the crystal and, for every atom, either its Bader charge (how much charge it carries) or its atomic density of states (which energies its electrons occupy). Of 1,024 structures per setting, counting only stable, unique, novel ones, 1.1% of the baseline's landed inside both a 2 eV band gap and a -4 eV per atom formation energy, against 3.5% of the density-of-states model's. Single targets gain more in points and less in proportion.

Why it matters: Asking a generator for a target property costs structural quality, and the electronic variable makes it cost less: at that two-target setting the share of stable, unique, novel structures falls from 33.3% to 21.8% with the descriptor and from 33.6% to 15.4% without it. The control experiments are the part to carry away. Corrupting the charges, or attaching a random number that means nothing, also raised the unique-and-novel share, so a novelty gain on its own shows only that something random was added, and it takes stability, validity and the target hit rate to tell physics from noise.

Evidence and limits: All of it is computed: structures are generated and scored by DFT, and nothing was synthesised or measured. A hit means landing within 0.5 eV, or 0.5 eV per atom, of the target. The validity filter is the authors' own caveat (about 30% of their training structures fail its charge-neutrality check), so both the stricter and the looser counts are reported. Several single-property gains are a few points, and some do not reach significance at 1,024 samples.

By Ibuki Okuda, Izumi Takahara, Teruyasu Mizoguchi

What happened: James Weber (University of Reading) and Makoto Kelp (University of Utah), equal authors, fine-tune Microsoft's Aurora on the UKCA chemistry-climate model, in three variants. The first reproduces 23 of 27 evaluated fields at R-squared of at least 0.85 in troposphere and stratosphere, with annual mean biases under 4% for ozone, CO and temperature; a simulated year takes 5.5 minutes on one GPU against 9.5 hours on 576 CPU cores. A second, trained on paired emissions scenarios, reproduces ozone's response to NO and CO. A third is trained on satellite ozone.

Why it matters: Chemistry is the most expensive part of a climate model, so it is usually simplified or fixed in advance, and trying many emissions scenarios needs it fast. The third variant is the interesting one: trained on Aura MLS satellite ozone and tested on years held out, it cut the error in ozone averaged around each latitude band by 23%, from 0.37 to 0.28 ppm, roughly 10 to 25 km up. That correction sits in the weights, so it carries to runs where no observation exists: an emulator can be better than the model it learned from.

Evidence and limits: Most of what is measured is agreement with UKCA, so a well-reproduced field inherits UKCA's own errors, and the species predicted are a subset the authors chose of UKCA's 100-plus. The satellite gain holds only in the latitude-band average: the same model is 9.6% worse at individual retrievals. The emission-response variant has its winds and temperature prescribed from the control run, and the 50-year run recycles one year of forcing.

By James Weber, Makoto Kelp

What happened: Hojae Choi, Samuel Pellock and David Baker at the University of Washington's Institute for Protein Design, with Donald Hilvert at ETH Zurich, report enzymes that use an activated cysteine to hydrolyse the polypeptide backbone in a sequence-dependent way. 13 of 69 designs worked, 19%, with rate enhancements over the uncatalysed reaction of up to 3x10^7. The folds are unlike natural proteases (TM score below 0.50), six crystal structures sit within 1.2 A of the design models, and the designs cleave their targets in mammalian cells.

Why it matters: A peptide bond in neutral water lasts hundreds of years, and de novo enzyme design had mostly managed reactions with low barriers. Cutting one where the designer chooses turns protein design into a way to make programmable scissors: pick the sequence, get an enzyme that cuts it. That is a different kind of tool from a binder, which is what designed proteins have mostly been.

Evidence and limits: Activity is still short of natural cysteine proteases: the best design, after optimisation, reaches 413 M-1s-1 against 350,000 for the Ulp1 the motif came from, which the paper puts down to imperfect active-site preorganisation and substrate positioning. In cells the proteases cut a substrate the authors designed and transfected in with them, not an endogenous protein, and the work is the designing lab's own.

By Hojae Choi, Samuel J. Pellock, Donald Hilvert, David Baker

What happened: Anqi Chen, Kejia Wu and David Baker at the same institute, again with Donald Hilvert at ETH Zurich, published the same day, used RFdiffusion to put a water-activating zinc site next to a peptide-binding pocket. Of 135 designs tested, 36% cut their target bond; the best beat the uncatalysed reaction by more than 10^8-fold, and four substitutions took it past 10^10. Designed proteases selectively cut the disease proteins TDP-43, amyloid-beta and serum amyloid A, up to 10^9-fold, and switch on caged cytokines.

Why it matters: The disease targets are the point: TDP-43 and amyloid-beta are proteins drug makers have tried to clear, and cutting one is a different kind of intervention from binding it. With the cysteine-protease paper beside it, the lab now reports two independent chemistries for the same hard reaction, one metal-based and one not.

Evidence and limits: Rate enhancements are measured against the uncatalysed reaction, not against a natural protease: out of the computer the designs run 10^3 to 10^4 below the best natural ones, two orders after four substitutions. The disease proteins were cut as purified protein in vitro, and nothing reaches a cell interior, an animal or a patient. The designers have filed two provisional patents on the methods and designs.

By Anqi Chen, Kejia Wu, Donald Hilvert, David Baker

What happened: The system pairs ACE, which runs the atmosphere at 100 km, with a new downscaler, HiROv2, that sharpens it to 3 km and now predicts several fields at once rather than rainfall alone. Given coarsened X-SHiELD data it matches that simulation's high-resolution statistics to within 3%, even for events that occur once in a million data points, and estimates tropical-cyclone energy for storms it never trained on. Over a 10-year run the hurricane energy is comparable to X-SHiELD's. The models, notebooks and a comparison demo are public.

Why it matters: Work on extreme-event risk needs many years of storm-resolving data, and Ai2 puts generating it with X-SHiELD at roughly 300 times the cost. At about a three-hundredth of the cost, a decade of global 3 km rain and wind becomes something a research group can generate itself, several times over, which is what estimating rare events takes.

Evidence and limits: Every number is measured against X-SHiELD, the simulation HiRO learned from, not against observations. Ai2 says HiRO only works with the ACE version tuned to X-SHiELD, so it cannot yet produce a warmer climate or reanalysis like ERA5, and winds and pressure around hurricane eyes stay more lopsided than in X-SHiELD.

What happened: A team led by Frances E. Reuland and Adam R. Brandt at Stanford released metered methane in Arizona as satellites passed overhead: 481 releases of 9.5 to 1,184 kg an hour and 265 blank controls, with the teams not told which was which. Fifteen teams from nine countries made more than 2,250 estimates from 12 government, commercial and non-profit satellites. Instruments built for methane detected 92-100% of releases with 0-11% false positives; repurposed ones 62-88%. Teams applying AI methods to Sentinel-2 and Landsat images made 79% of all false positives.

Why it matters: Satellite plume reports already feed leak alerts and national methane rules, and the EU's proposed import standard for gas leans on them, yet Carbon Mapper's Tanager-1, EMIT, GHOSt and the AI detection methods had had little or no blind testing. This one gives each a measured detection rate and error against known releases, and leaves an open dataset for training the next detectors.

Evidence and limits: Teams using AI scored 0.62-0.77 accuracy, better than chance, but made most of the false alarms, and the authors say AI methods need more work before routine use. Releases were at a known site: the authors' conclusion holds when the source location is roughly known, and cloud cover over 20% degraded every system.

By Frances E. Reuland, Adam R. Brandt

What happened: Iambic sold 9,375,000 new shares at $16 for $150M gross, and the underwriters can buy 1,406,250 more within 30 days. The shares started trading on 15 October as IAM, opened at $16.15 and closed at $18.91, 18% above the offer price; on 16 October they closed at $23.81. J.P. Morgan, Jefferies, BofA Securities and Citigroup ran the books. Tom Miller, co-founder and CEO, and Fred Manby, co-founder and CTO, lead the company.

Why it matters: Public investors have now put a price on an AI drug-discovery company whose most advanced drug, the HER2 inhibitor IAM1363, is still in Phase 1. The prospectus puts the money, with cash on hand, toward finishing that trial and starting Phase 2 and 3 trials, taking IAM217 and IAM-C1 through Phase 1 dose escalation, and the AI platform, and says it lasts into the fourth quarter of 2028. By the prospectus's share count, $16 a share values Iambic at about $760M.

Evidence and limits: $150M is before fees; the offering was due to close on 16 October and the extra-share option is unexercised. The price says what investors will pay, not whether Iambic's AI-designed drugs work.

What happened: Minos turns Leash's DNA-encoded-library screens, millions of molecules measured against many proteins, into environments where an agent chooses molecules within a budget and is told what was measured. The flagship task gives no starting molecule and a budget of 100 molecules over 20 rounds, across 33 targets with about 6 million molecules each. Leash reports about 100,000 agent trajectories in six weeks, releases a few environments and thousands of reasoning traces, and finds that stronger models do better and weaker ones stay too long in poor regions of chemical space.

Why it matters: Because every molecule was measured in advance, an agent's choices cost compute rather than lab weeks, and the results of the molecules it did not pick are known too, so its decisions can be judged, not only its final answer. Leash CEO Ian Quigley's argument is that data collected this way is reusable experience, in the way game moves are for a game-playing model.

Evidence and limits: All results are Leash's own; agent performance is shown in figures this page did not read values from, and the preprint was not opened. The environments cover binding to one target among molecules already screened, with no pharmacokinetics or toxicity, and the published ones are a small sample of what Leash holds.

What happened: Probabilistic bias correction (PBC) learns from a forecast system's past predictions and the weather that followed how to shift its probabilities. Over 2016-2024 it improved ECMWF's already debiased dynamical model on 91% of pressure, 92% of temperature and 98% of precipitation targets; on the 2025 test year it doubled the skill of ECMWF's AI system. Entered as MicroDuet in ECMWF's AI Weather Quest, it scored best for every variable and both lead times in the September-November 2025 season, and best again in the winter season that followed.

Why it matters: Two to six weeks ahead is where farmers, grid operators and flood planners need a forecast and where models do worst, often no better than the long-term average for the date. PBC is a layer that works on top of any model, and its contest scores come from forecasts issued before the weather happened, set against operational centres' own systems. Hannah Guan and Soukayna Mouatadid led it, with Judah Cohen of MIT and Lester Mackey of Microsoft Research; Cohen says Kenya's meteorological office has asked about using it.

Evidence and limits: The contest covered two seasons, and ECMWF, which runs it, has two authors on the paper. For precipitation, all six centres' debiased forecasts scored below the climatological average, so the gain starts from a low base. Better weekly-average scores do not yet show a decision made better, and the paper reports no operational use yet.

By Hannah Guan, Soukayna Mouatadid, Judah Cohen, Lester Mackey

What happened: Danaher's release says the two are working to combine its instruments, automation and lab workflow software with NVIDIA's computing: edge hardware such as DGX Spark, RAPIDS for analytics, and real-time inference and foundation models including the BioNeMo Open Models. Kimberly Powell of NVIDIA states the loop the release describes, experiments improving models and models designing experiments; Julie Sawyer Montgomery of Danaher says AI is reshaping how science is done. Initial efforts cover a select group of priority instruments.

Why it matters: Danaher sells instruments that sit in a great many labs, so where its boxes compute matters: an autonomous lab needs the measurement and the inference close together, and edge hardware at the instrument is one way to get there. The release names the parts rather than a product. It stays an intention until something ships or a customer says what changed.

Evidence and limits: No money, no equity, no timeline and no product. Which instruments are in the select group is not named, the wider portfolio is only a possibility, and the Cosmos world models and Omniverse digital twins are described as being explored. NVIDIA has issued no release of its own. Danaher's separately announced autonomous antibody lab is still at the plans stage, expected to run at scale in early 2027.

What happened: Across 15 targets and 3,666 designs, folding only the contact shell plus a margin reproduced the full target's interface to a median 0.37 angstrom, and cut GPU hours by 88% on the 719-residue outer part of fibroblast activation protein alpha. With a researcher setting the strategy and a Gemini 3.1 Pro agent choosing the crops and parameters, the in-software hit rate rose from 5.06% to 9.33% while saving a further 42%. Yeast display then gave four clones at apparent 3.0 to 9.1 nM.

Why it matters: AlphaFold3 charges by token bucket, so most of the compute in screening designs goes on target residues far from the binding site, and a big outer domain is what makes a target impractical to design against. The paper treats cropping as something to test rather than a free saving: whether the interface geometry survives, whether the ranking that separates binders from non-binders survives, and whether the saving crosses a bucket boundary. Left to pick its own strategy from a map of the whole target, a fully autonomous arm converged on the same catalytic-rim approach the researchers had specified.

Evidence and limits: Cropping cost ranking: mean interface ROC-AUC fell from 0.777 to 0.729, and on one target the ranking collapsed, recovered only by dropping the target's alignment, which helped deep alignments and hurt a shallow one. Seven of the 15 targets were small enough to gain nothing. Affinities are apparent values on the yeast surface, not purified protein; one target carried both the wet lab and the agent arms, nobody outside repeated it, and this is a technical report.

By Jun W. Kim, Annalisa Pawlosky

What happened: Generate takes $30M upfront, up to $35M in early preclinical milestones and milestones of nearly $1.2B in all, plus tiered royalties, for two initial programmes with an option on a third. Generate runs research through lead-candidate selection; Zealand takes clinical development, regulatory filings and worldwide commercialisation. Utpal Singh, Zealand's chief scientific officer, says proteins complement its small molecules and peptides; Mike Nally, Generate's chief executive, says the teams will define the target profile first.

Why it matters: Obesity is where the metabolic money is, and Zealand is buying protein design rather than building it: its own 25 years are in peptides. For Generate it is the third pharma collaboration after Amgen in 2022 and Novartis in 2024, and the first since it listed on Nasdaq in February, which is the kind of revenue a newly public platform company needs.

Evidence and limits: The $1.2B is what could be paid if programmes reach development, regulatory and commercial milestones; $30M is what is paid now. No target, candidate or data is named, and the work starts at design.

What happened: New investors Delta-v Capital, Socium Ventures, HighSage Ventures, Coral Capital and Legion VC joined, and Coatue, J.P. Morgan Growth Equity Partners, Index Ventures and Applied Ventures followed on. CEO and co-founder Nick Talken says the money expands Albert's chemistry AI; Morgan Gerlak of TCV spoke for the lead. The partners the release names include AkzoNobel, BASF, Henkel, Kenvue and Clorox.

Why it matters: Albert sells to the makers of coatings, adhesives and consumer goods: AI that searches their own formulation records, suggests candidate formulations and designs experiments, rather than discovering new molecules. The evidence that its model helps is Albert's own: a white paper says that on the hardest set of a benchmark Albert built, its retrieval model Valence put the right passage from about a million drawn from US patents in its top 100 results 81.8% of the time, against 2-4.5% for OpenAI and Voyage models. No valuation was given.

What happened: Pawel Stankiewicz's lab at Baylor College of Medicine gave Gamow Labs de-identified whole genomes from 26 infants whose lung disease first-line labs had not diagnosed; the lab had solved 19. Gamow's George-0.1 pipeline, built on frontier models including Gemini 4 Argon, matched all 19 and flagged candidates in 2 of the other 7. In one, read evidence pointed to a balanced translocation separating FOXF1 from its enhancer, consistent with alveolar capillary dysplasia; Sanger sequencing in Stankiewicz's lab confirmed it.

Why it matters: The step forward is that a candidate the AI proposed went on to be confirmed in a lab: Baylor's own sequencing verified a chromosomal rearrangement that had gone undiagnosed for three years. That is the step from flagging a variant to a diagnosis a family can act on.

Evidence and limits: It is a small study, 26 cases, disclosed by the company in a post rather than a paper, and the outcome of the second candidate is not given. Argon is one of several models in the pipeline, so the result belongs to the pipeline, not to Argon alone.

By Daniel McKinnon

What happened: On 13 October Google opened Gemini 4 Argon to developers, enterprises and eligible subscribers, and invited 10,000 scientists to apply for six months of free access to Antigravity's top usage tier. Its science case studies cover enzyme design, quantum computing, fusion research and rare-disease genome analysis.

Why it matters: What stands out is less the model's scores than frontier AI beginning to leave traceable results in different research workflows. The fusion work has a public preprint, and the speed-up of the quantum chemistry package xTB has code submitted to its repository; Gamow Labs reports that its AI pipeline found a previously undiagnosed chromosomal rearrangement, which a partner lab confirmed by sequencing.

Evidence and limits: The cases rest on different kinds of evidence. The fusion paper is still a preprint; the three xTB pull requests are open, not merged, and a submission is not an independent check of the speed-up; Gamow's study is the company's own account, and Argon is one model in a multi-model pipeline. The enzyme activity and some quantum results lack public material an outside group could check.

What happened: Xiyao Yu of Nanyang Technological University shows that the usual test, force error on equilibrium snapshots, barely sees the barrier region that sets a rate, so worst-case rate error grows exponentially with barrier height. Weighting energy errors by a 'leverage density' concentrated on reaction paths instead gives an estimated interval for the rate. In alanine dipeptide, five designed perturbations of matched force error changed the rate 0.49 to 3.5 times; in Li3OCl the intervals ordered six models' ion-hop rates, but five measured rates sat about 7% above the upper end.

Why it matters: Machine-learned potentials are judged by force error but used to compute diffusion and reaction rates. This offers a check costing a few hundred reference energy calculations, reported as two numbers beside force error, and picking training data by it cut rate errors even as force errors rose. The Li3OCl agreement holds only once hops are sorted by mechanism; on the pooled set the intervals do not track. Every rate tested against is the author's own simulation, not experiment, in an unreviewed single-author preprint.

By Xiyao Yu

What happened: Fan Nie, Haotian Ye and colleagues split verification into proposing candidate errors and then judging whether each is real, with the threshold set so that almost no valid proof is wrongly rejected. Across three maths benchmarks that cuts the invalid solutions a reviewer must read by 46% on average against a voting baseline at the same false-rejection rate, and by 57% once the validator is trained. Pointed at Xinwen Zhu's 2017 Annals of Mathematics paper, it reported 45 issues where the same model reviewing directly found 11.

Why it matters: Expert review becomes the scarce resource once a solver produces more candidate proofs than anyone can read, and this treats that capacity as a budget: reject as much as possible while letting through almost every valid proof. The authors of the papers it checked confirmed the errors and revised. Paired with a solving agent it also helped settle three open conjectures in relative Langlands duality, which experts then confirmed.

Evidence and limits: The 11 preprints' main conclusions hold after the corrections. The confirmations come from the papers' own authors, and for the preprint credited with 88 issues one of them is Shurui Liu, an author of this paper; he also wrote up the three Langlands proofs, co-wrote the Manin constant paper and co-wrote the solver OSVerify is paired with. No formal proof checker is used anywhere.

By Fan Nie, Haotian Ye

What happened: Bloomberg, citing people familiar with the matter, says the round is worth tens of millions of dollars and would value Winverse at about $400M after the money, with Meituan's Long-Z Investments also committed. The deal is not final and its terms could change. Han Hu founded Winverse this year after leading Tencent's vision-language and embodied foundation model teams; he was earlier a principal researcher at Microsoft Research Asia. The company is still in stealth.

Why it matters: It shows a model lab putting money into the AI that lets robots act, not only into robot hardware: DeepSeek already took a stake in Unitree when the humanoid maker listed in Shanghai. Bloomberg's point is that China builds robot bodies well, while the models that turn instructions into reliable actions lag. Nothing has closed, none of the parties commented, and Winverse has shown no product or result.

What happened: Matt Rosseinsky of the University of Liverpool's chemistry department founded the company. It sits in the university's Materials Innovation Factory and pairs AI prediction with experimental and characterisation expertise, aimed at inorganic crystals for batteries and catalysts. IP Group led the oversubscribed round, with Northern Gritstone, the University of Liverpool Enterprise Fund, Norrsken Evolve, LYVA Labs and the Parkwalk-managed Northern Universities Venture Fund alongside. The money is for the team, the platform and commercial partners.

Why it matters: Matter Machine represents a different commercial route for materials AI: pairing model predictions with a university's existing synthesis and characterisation capability. Its founder's Liverpool lab has repeatedly made new materials that computation pointed to, and the round adds an executive the release says helped build Oxford Nanopore.

Evidence and limits: The round funds the team and the platform. The company has not yet published material data of its own from which its technology could be judged.

What happened: Four departments and agencies say pilots have helped identify and fix over 1,000 potential vulnerabilities in government systems, and the programme will grow. The government will explore using the Cyber Security and Resilience Bill to have serious AI incidents and near misses identified and reported, and to cover loss-of-control risk, where autonomous systems evade human oversight. The AI Security Institute and the National Cyber Security Centre are already looking at how to respond to incidents involving AI agents. AI Minister Kanishka Narayan announced it.

Why it matters: AI safety governance is extending from testing models before release to reporting incidents and responding after deployment. That matters for the science agents the record follows, which are starting to run code and lab equipment on their own.

Evidence and limits: The vulnerability pilots are already running, and the 1,000 figure is the government's own, with no method or model named. Mandatory reporting of serious incidents is still being explored, with measures to be worked through with parliamentarians, technical experts and operators.