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Thursday30 items

  1. Greg BrockmanResearch
    AI score58

    Greg Brockman shares new lead from GPT-Rosalind in drug discovery

    Greg Brockman shared OpenAI's progress on GPT-Rosalind: the model has surfaced a new lead in drug discovery, namely whether restoring cholesterol transport could protect neurons from degeneration. OpenAI says DCTN1 is a gene associated with Perry syndrome, and that disrupting it in mouse brain data increased the expression of genes involved in cholesterol synthesis; on this basis, GPT-Rosalind hypothesized that cholesterol may not be reaching where neurons need it. In collaboration with PerturbAI and Scripps Research, thousands of GPT-Rosalind agents worked in parallel, linking gene expression results from millions of mouse brain cells with scientific literature within 32 minutes, and OpenAI says it is expanding this collaboration to find new drug targets.

    Quoted postOpenAI · @OpenAI

    GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in making cholesterol. GPT-Rosalind suggested a possible explanation: cholesterol may not be reaching where neurons need it. That gives scientists a specific mechanism to test and, if confirmed, a direction for developing new treatments for neurodegenerative disorders. With @perturbai_tx and @scrippsresearch, a team of thousands of GPT-Rosalind agents worked in parallel, connecting gene-expression results from millions of mouse brain cells with scientific literature in just 32 minutes. We’re now expanding this collaboration to identify new drug targets. https://t.co/oHdF3ELsMj

    Same story · 5 reports

    #English text is being prepared.#Papers/Research#Agents

  2. Microsoft ResearchFirst-partyResearch
    AI score78

    Microsoft Research proves the 31-year-old unsolved KLS conjecture

    Microsoft Research researcher Xinzhi Zhang and co-authors proved the KLS conjecture proposed in 1995, giving a dimension-independent fixed bound; the previous best result only allowed the bound to grow slowly with dimension. The proof came from a human-guided agent search process, in which the team explored more than 100 candidate paths, with researchers evaluating, pruning, and redirecting them; an independent team also gave a complete proof at the same time, followed by Lean 4 formal verification. The authors say earlier unguided end-to-end attempts with multiple frontier models failed to achieve the goal.

    Same story · 3 reports

    #Papers/Research#Reasoning#Microsoft

  3. Google ResearchFirst-partyResearch
    AI score64

    Google Research uses AI to assist blind-sweep ultrasound assessment of gestational age, with error comparable to experts

    Google Research published a study in JAMA Network Open; its AI gestational age assessment model based on blind-sweep ultrasound achieved a mean absolute error of 4.18 days in prospective testing in Chicago (n=192) and Nairobi (n=193), reaching non-inferiority compared with expert sonographers' 4.48 days. The model was fine-tuned only on data from 120 participants in Chicago and did not use Nairobi data; the errors in the two locations were 4.10 days and 4.27 days, respectively. The study used a Clarius C3 handheld probe, with standardized blind sweeps performed by operators without professional ultrasound training.

    Bar chart comparing the mean absolute error between the AI model and expert clinical standards across multiple cohorts.

    #Papers/Research#Image generation#Google

Wednesday22 items

  4. Rohan PaulResearch
    AI score76

    OpenAI and others use 1,946 agents to interpret a mouse whole-brain CRISPR atlas in 32 minutes

    OpenAI, PerturbAI, and Scripps Research used 1,946 GPT-Rosalind-5.5 agents to complete interpretation of a mouse whole-brain CRISPR atlas in 32 minutes; OpenAI estimates that manual work would take months. Each agent analyzed only the effect of a single gene knockout and retrieved literature, and did not access the atlas paper published after the model's December 2025 knowledge cutoff, yet still matched or refined 48 of 55 published findings supportable by the expression data. The core of the method is to split one giant analysis into nearly 2,000 single-gene small tasks executed in parallel; a single agent processing 893 genes cited only 11 papers, while after splitting the citation count was 711 times that, and blinded PhD reviewers preferred the split reports in 75% of cases.

    Quoted postOpenAI · @OpenAI

    GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in making cholesterol. GPT-Rosalind suggested a possible explanation: cholesterol may not be reaching where neurons need it. That gives scientists a specific mechanism to test and, if confirmed, a direction for developing new treatments for neurodegenerative disorders. With @perturbai_tx and @scrippsresearch, a team of thousands of GPT-Rosalind agents worked in parallel, connecting gene-expression results from millions of mouse brain cells with scientific literature in just 32 minutes. We’re now expanding this collaboration to identify new drug targets. https://t.co/oHdF3ELsMj

    Same story · 5 reports

    #Papers/Research#Agents#OpenAI

  10. 36Kr · AIResearch
    AI score66

    Mass General Brigham team publishes FAHR-Face study in The Lancet Digital Health, using facial photos to predict mortality risk in cancer patients

    The U.S. Mass General Brigham health care system developed the FAHR-Face model, which identifies differences in mortality risk among cancer patients using only a single facial photograph; the study was published on October 20 in The Lancet Digital Health. Among 3,820 cancer patients who did not participate in training, 64 of the 122 people rated by the model as extremely high risk died within 90 days after the photo was taken, while only 20 of the 975 people with scores between 0 and 0.1 died, a rate of 2.1%. The study also conducted external validation using 4,892 patients at the Maastro radiotherapy center in the Netherlands and obtained similar results, but the model cannot yet be used directly in clinical practice.

    #Papers/Research#Multimodal#English text is being prepared.

  14. Rohan PaulResearch
    AI score79

    Claude autonomously designed 17 molecules that bind scorpion venom neurotoxins in Claude Science

    In Anthropic's Claude Science environment, Claude autonomously completed an entire round of protein design as an agent: it selected the attack sites on scorpion venom neurotoxins on its own, drove 12 protein design programs, set scoring thresholds, and selected 180 molecules from the 318,484 generated to send for physical synthesis; of the 211 designs tested by Adaptyv Bio and Twist Bioscience, 17 successfully bound the target toxin. The request was made by Citrini Research, a financial firm with no laboratory or protein expertise. Anthropic did not call it an antivenom serum because the tests needed to support that claim have not yet been conducted.

    Quoted postAnthropic · @AnthropicAI

    New on our Science Blog: designing protein binders against scorpion neurotoxins. Scorpions sting about 1.2m people every year, and antivenoms are often costly or in short supply. Claude has made an early step toward finding new treatments. https://t.co/l6LgdEqM7I

    #English text is being prepared.#Agents#Anthropic

  16. Rohan PaulResearch
    AI score56

    Meta's new paper proposes the ProPerMem benchmark: proactive assistants struggle with autonomously judging when to interrupt

    A new Meta paper points out that the failure point of proactive assistants (such as AI glasses) lies in autonomously judging when to speak, not just what to say. The team built the ProPerMem benchmark, using first-person video paired with interference-rich, full-year memory averaging 618K tokens, and featuring human-verified trigger windows. All baselines scored at most 15%, even frontier models using retrieval-augmented memory; when oracle evidence was provided within the window, GPT-5.3-Chat reached 96%, but when it was left to choose the timing itself, this dropped to 38%, and to 15% after adding retrieval. Grasping both content and timing while reducing trigger frequency can raise accuracy from under 10% to 42.4%.

    #Papers/Research#Agents#Multimodal