From better modelsto tighter scientific loops

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: ALLOX closed a $10.5M (€9.4M) seed led by Asabys through its Sabadell Asabys II fund, with Spain's innovation agency CDTI, through Innvierte, and the Institut Català de Finances. It came out of Barcelona's Centre for Genomic Regulation; Júlia Domingo is CEO and co-founded it with André Faure, Pablo Baeza and Ben Lehner. Morten Graugaard, chief business officer of Orbis Medicines, becomes independent chair, and Asabys co-founder Clara Campàs sits on the board. The money goes to its mutation-mapping platform, its own AI models, a drug discovery team and small-molecule programmes.

Why it matters: Many disease proteins, such as transcription factors and proteins that work by gripping other proteins, offer no pocket for a drug to sit in. ALLOX looks for other spots on the protein that still change what it does, by measuring thousands of mutations at once. Faure, Domingo and Lehner published the method in Nature in 2022: neural networks fitted to those measurements mapped such sites across two common human protein domains. The round pays for AI models that predict these sites from the company's own data.

Evidence and limits: A financing, not a result: the release names no target or new data, and the company site names one preclinical inhibitor, ALX-001, with nothing shown. The 2022 paper mapped two small protein domains in the lab; nothing public yet shows a drug made against a site found this way.

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: Existing investors Shanghai Biomedical Innovation Fund and Fortune Venture Capital put in more; Guokai Kechuang, Ruili Investment and Guofang Capital joined. The Beijing company, founded by Jian Tang of Mila and HEC Montréal, gives no size for this round, only that its A series, which includes a June round of several hundred million yuan, now tops $100M. The money is for GeoFlow, its molecule model, GeoCell, a model of how cells respond to perturbations, a larger automated lab and its own pipeline.

Why it matters: The round funds a move from designing a molecule to choosing what it is for: GeoCell is meant to say which target, which disease and which patients a design suits, and the automated lab is to feed results back into both models. Qiushan Guo, a partner at the Shanghai fund, which co-led the June round, said the company has carried its models from molecules to cells. The only revenue it puts a date on comes from synthetic biology, not drugs: it says biosynthesised borneol, 80% cheaper than plant extraction, will bring in revenue at scale in 2027.

Evidence and limits: The $100M is the A series as a whole, not this round. The IND filings for several bispecific antibodies in 2027, the borneol cost cut and the June claim of a nearly 20% hit rate across more than 20 antibody targets are the company's statements, with no data shown.

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 Benjamine 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: Creandum led the seed, and its partner Katharina Wilhelm joins the board. The company also disclosed an earlier $2.5M pre-seed led by First Momentum Ventures and LEA Partners, for $12.5M in all. Simon Romanski (CEO) and Sebastian Bayer (CTO, who led language-model research at Google) founded it in 2024. Its model, trained on about 2 million RNA readouts from human tissue, predicts how each patient in a virtual cohort responds to a treatment. The money goes to hiring, validating the platform and new pharma clients.

Why it matters: Phase II is where most drugs fail, and where a developer learns which disease and which patients a molecule suits. Atlas wants to answer that before the trial, by running the same virtual patients through several treatments and diseases, a comparison no real trial can make. It is the second in silico trial company to raise money in two days, after adsilico's virtual patients for heart devices; this one is for drugs, starting in immunology. Wilhelm calls clinical development the second wave of AI in drugs, after drug design.

Evidence and limits: The case studies on the company's site, including a predicted Phase II miss that the trial then confirmed, are its own, with methods and cohorts shown to partners only under NDA. That a Basel client skipped animal tests on the strength of its simulation is the company's statement, and the client is unnamed.

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: The San Francisco company, in Stanford's StartX summer 2026 cohort, puts a drug developer's trial, RNA-seq, variant and other data beside published studies, and runs and writes up the analysis a scientist asks for in plain language, starting with teams planning the next trial from completed ones. CEO and co-founder Elena Cassidy, Bright Ventures founder Lenore Champagne Beirne and scientific adviser Michael Snyder spoke in the release. The money goes to the platform and to hiring scientists and engineers.

Why it matters: It is a bet that the slow step in clinical development is analysis, not data: companies already hold the trial and genomic results that could explain who responded, but they sit in separate studies and systems. Omabit sells the joining-up and the analysis, including predictive models built on the company's own data, rather than new data.

Evidence and limits: The site's claim that an analysis takes a day instead of three months is the company's, with no case shown. No customer is named.

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: PXN Ventures led the round, with existing investors Northern Gritstone, which put in £5M, and Parkwalk. adsilico was founded in 2024 by Sheena Macpherson, its CEO, Nishant Ravikumar and Zeike Taylor. Its Virtual Chimera platform builds virtual hearts and blood vessels from anonymised scans with generative AI and runs physics simulations of a device inside each. The money goes to the heart models, neurovascular devices and a team growing from 14 to 34.

Why it matters: A device trial enrols a few hundred patients, so many body types are never tested. adsilico's answer pairs a generative model of anatomy with physics simulation, an in silico trial: the device is tested on thousands of virtual bodies before real ones, in a field where regulators decide what evidence counts.

Evidence and limits: The company names completed projects with heart-valve and neurovascular device makers but no customer, and no validation of the virtual patients against real outcomes is cited. That PXN led comes from Tech.eu; Northern Gritstone's own post lists the investors without saying who led.

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: A grant from the WoodNext Foundation, the philanthropy of Roku founder Anthony Wood and Susan Wood, funds the Aggie AI Foundry in the university's materials science department. AI trained on physical models and past alloy work proposes compositions, and robot arms mix, melt and test them at up to 3,000 °C. Department head Ibrahim Karaman and Raymundo Arróyave lead it; the university says it will cut alloy development from months to a week or less.

Why it matters: The university says existing self-driving labs handle materials that are easy to make and process, while new alloys have to be melted and tested at very high temperatures; Arróyave adds that no computer can yet predict how one will behave. A lab that runs that whole loop for metals would test whether AI-led, robot-run discovery can reach the structural materials used in energy, aerospace and medicine.

Evidence and limits: This is a grant to build the lab, not a working lab: no opening date, throughput or result is given. That it is the first lab of its kind in academia, and the months-to-a-week speed-up, are the university's own claims.

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