Benefit, harm, and capability ledgers
Events
Frequency · stacked by domain
Events over time
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- Chemistry
- Climate & Environment
- Computer Science
- Economics & Productivity
- Education & Knowledge Access
- Engineering
- Materials Science
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Heatmap
Domain by month
Significance
Impact tiers
Base impact
- T1 · 1Useful / Limited
- T2 · 3Notable / Significant
- T3 · 10Major
- T4 · 30Historic
- T5 · 100Civilizational / Catastrophic
Base points before attribution, evidence, realization, durability, and credit share.
MethodologyFrequency
Benefit versus harm counts
Politicians have failed to tackle health care affordability. It’s now voters’ rallying cry
As costs surge, voters are embracing more radical solutions to the crisis.
The brain liquidates billions of mutant neurons around the time we’re born
The brain liquidates billions of mutant neurons around the time we’re born
‘Elonville’: US residents say Musk’s xAI offered to buy homes if they don’t sue over datacenter noise - The Guardian
‘Elonville’: US residents say Musk’s xAI offered to buy homes if they don’t sue over datacenter noise The Guardian
Family sues OpenAI after Seattle woman dies following medication taper - SILive.com
Family sues OpenAI after Seattle woman dies following medication taper SILive.com
Daily briefing: The big push to personalize psychiatry
Daily briefing: The big push to personalize psychiatry
Short-term academic visits to China could sustain ties
Short-term academic visits to China could sustain ties
AI-hallucinated citations should be treated as research misconduct
AI-hallucinated citations should be treated as research misconduct
Social Media Victims Law Center and The Schenk Law Firm File Wrongful Death Lawsuit Against OpenAI Alleging ChatGPT’s Medical Advice Resulted in the Death of a Woman with Epilepsy - Yahoo Finance
Social Media Victims Law Center and The Schenk Law Firm File Wrongful Death Lawsuit Against OpenAI Alleging ChatGPT’s Medical Advice Resulted in the Death of a Woman with Epilepsy Yahoo Finance
Claude-orchestrated Divergence Labs campaign produces experimental IgE-disruptor VHH hits
On 27 October 2026 Anthropic and Divergence Labs reported a Claude-directed campaign that generated about 35,000 VHH designs toward a biparatopic IgE disruptor for food allergy. Competitive BLI found 20/91 disruptor hits, three of which removed more soluble IgE than omalizumab at 25–400 nM, plus three de novo anchors. The arms have not been paired, cells and animals were not tested, and the designs did not beat parent sdAb026 at controlled doses.
OpenAI expands partnership with Oracle to bring its apps to ChatGPT
OpenAI and Oracle are expanding their partnership to bring business applications and AI agents into ChatGPT and power new Oracle offerings.
Former DOGE Staffers Use AI Agents to Breach Anthropic’s Internal Systems - Security Boulevard
Former DOGE Staffers Use AI Agents to Breach Anthropic’s Internal Systems Security Boulevard
Retell uses OpenAI models to help power 100M monthly calls
GPT-6 Astra tops Retell’s voice agent benchmark, while Codex helps it reach a $1.6M annual revenue run rate per employee.
Methane emissions remain flat even as biggest US oil region opens the tap
Methane emissions remain flat even as biggest US oil region opens the tap
Mysterious flickering brain signals could help to unlock secrets of consciousnes
Mysterious flickering brain signals could help to unlock secrets of consciousnes
STAT+: RFK Jr. goes for broke in Iowa
Kennedy on the campaign trail for MAHA, and new rules loom for U.S. investments in Chinese biotech.
Author Correction: A chemical defence against phage infection
Author Correction: A chemical defence against phage infection
Can scientists reliably tell if a novel is written using AI?
Can scientists reliably tell if a novel is written using AI?
A possible treatment for brain-eating amoebas, found in the sea
Brain-eating amoebas are hard to treat and nearly always fatal. But researchers have isolated a compound that could help stop the disease.
AI watermarking efforts must encourage independent public scrutiny
AI watermarking efforts must encourage independent public scrutiny
Daily briefing: The Selfish Gene, 50 years on
Daily briefing: The Selfish Gene, 50 years on
Nerve activity initiates autoimmune hair loss
Nerve activity initiates autoimmune hair loss
ChatGPT’s expanding world of work
Explore OpenAI Signals data on 165 work activities across nearly 100 countries, revealing how people use ChatGPT for work and how usage changes over time.
Anthropic’s Claude bypasses paywall to access Georgia voter data without authorization - TradingView
Anthropic’s Claude bypasses paywall to access Georgia voter data without authorization TradingView
Anthropic’s delay in reporting ‘rogue agent’ Georgia vote hack alarms government officials - New York Post
Anthropic’s delay in reporting ‘rogue agent’ Georgia vote hack alarms government officials New York Post
How Ultrafast cut incident investigation from hours to minutes at OpenAI
See how OpenAI engineers use Codex Ultrafast to investigate incidents in minutes. Explore faster security analysis, code refactoring, and daily workflows.
Milestone cancer vaccine success leaves hard questions unanswered
mRNA vaccine from Merck and Moderna slashes risk of melanoma’s return, but why remains unclear
STAT+: Incyte pancreatic cancer medicine induces lasting tumor responses, company says at ESMO
A targeted drug from Incyte, when used in combination with chemotherapy, induced lasting tumor responses in certain patients with pancreatic cancer
OpenAI announces three AI-assisted research claims across mathematics, computer science, and physics
OpenAI says its internal models produced results on all-dimensional local smoothing, the Exponential Time Hypothesis, and quantum-field-theory existence. Two writeups and Lean files are public; the physics work remains under academic review and is not yet released.
Claude’s Voter-Data Incident Raises Fresh Questions over US AI Security Oversight - تسنیم
Claude’s Voter-Data Incident Raises Fresh Questions over US AI Security Oversight تسنیم
STAT+: Rising premiums, a sad Medicaid gathering, and a new fraud crackdown
The cost of health insurance is surging, and CMS presses ahead with a new fraud crackdown.
Mentoring a lab colleague? Share your ‘CV of failures’ with them
Mentoring a lab colleague? Share your ‘CV of failures’ with them
How an AI start-up founded by ex-DOGE staffers broke into Anthropic - The Washington Post
How an AI start-up founded by ex-DOGE staffers broke into Anthropic The Washington Post
STAT+: Nonprofit Bon Secours Mercy Health strikes $5.7 billion deal for surgery center chain
The deal would make the Catholic system one of the largest operators of outpatient surgery centers.
Tradeoff-Aware Territory Design: A Multi-Objective Decision System for Enterprise Customer-to-Seller Allocation
arXiv:2610.31701v1 Announce Type: new Abstract: Enterprise customer journeys are shaped not only by product experiences and marketing, but also by recurring resource-allocation decisions. In B2B go-to-market (GTM) organizations, territory carving determines which accounts receive seller coverage, which seller-account relationships are preserved, and how limited field capacity is distributed. Yet the process often remains manual, making it difficult to compare opportunities, value continuity, and evaluate tradeoffs among growth, balance, capacity, and feasibility. We present a production dec
Surprise! White dwarf stars come in a rainbow of colours
Surprise! White dwarf stars come in a rainbow of colours
AI doctors’ orders come with a sky-high bill
AI doctors’ orders come with a sky-high bill
Can plants defend coasts against storms?
Can plants defend coasts against storms?
Teen depression and anxiety predicted by childhood brain development
Teen depression and anxiety predicted by childhood brain development
This ‘glue gun ant’ fires sticky webs at its enemies
Lordomyrma azumai has repurposed its venom gland as a web slinger
Claude research campaign produces 17 experimentally validated scorpion-toxin binders
Anthropic reports that an unreleased Claude Science model generated 318,484 protein designs in 64 hours; external labs tested 211 and 17 met strict binding criteria against individual scorpion toxins. No venom neutralization or antivenom has been demonstrated.
STAT+: Top immunology association divided over $8.4-billion California ballot measure to fund the field, emails show
Internal emails obtained by STAT show that the American Association of Immunologists is sharply divided over California's Proposition 38, even though it would provide $8.4 billion in taxpayer dollars for…
Why failure is a taboo topic in science
Why failure is a taboo topic in science
How pregnancy reshapes the brain throughout life
How pregnancy reshapes the brain throughout life
Opinion: My mother killed herself. I wish assisted suicide had been an option
“Sometimes mental illness is terminal,” Torie Bosch writes in a personal essay about medical aid in dying for psychiatric reasons.
AI Breach of Australian Healthcare Website Sparks Regulation Debate - Carolina Political Review
AI Breach of Australian Healthcare Website Sparks Regulation Debate Carolina Political Review
OpenCode Security Breach: Distillation Attack Hit Console Users With Linked AI Providers. How to Find OpenCode on Developer Machines and Respond - Step Security
OpenCode Security Breach: Distillation Attack Hit Console Users With Linked AI Providers. How to Find OpenCode on Developer Machines and Respond Step Security
History’s long-secret codes are being cracked by amateurs using AI
History’s long-secret codes are being cracked by amateurs using AI
How Itaú is building generative AI for its entire customer base
With ia.i, a conversational interface, and the IARA governance platform, Itaú is applying its AI First strategy to customers and employees — in one of Brazil’s most regulated sectors.
PhD students who write dissertation with AI less likely to stay in academia
PhD students who write dissertation with AI less likely to stay in academia
OpenAI discloses four more rogue incidents involving financial, medical, and government data - Fortune
OpenAI discloses four more rogue incidents involving financial, medical, and government data Fortune
Daily briefing: We’re close to reaching a 30% land-conservation target
Daily briefing: We’re close to reaching a 30% land-conservation target
STAT+: Pharmalittle: We’re reading about an HHS peptide push, Trump’s pharma stock holdings, and more
The Trump administration is proceeding with a plan to allow compounding pharmacies to produce certain peptides
Memory stays strong in old age when this brain enzyme is active
Memory stays strong in old age when this brain enzyme is active
Immorta Bio says AI helped identify SenoMune-0257, a nine-amino-acid senolytic peptide
On 12 October 2026 Immorta Bio announced it had finished planned preclinical studies of SenoMune-0257, a synthetic nine-amino-acid peptide it says AI helped select to stimulate T cells against senescent cells. The company aims for first human evaluation before year-end 2026. No sequence, named model, public data package, or trial site was disclosed.
‘When prevention disappears, infections rise’: can the world still end AIDS?
‘When prevention disappears, infections rise’: can the world still end AIDS?
Retrofitted LLM can count the letter ‘i’s in ‘artificial intelligence’
Retrofitted LLM can count the letter ‘i’s in ‘artificial intelligence’
Scientists take a radiometer for a spin in a hot-air balloon
Scientists take a radiometer for a spin in a hot-air balloon
OpenAI releases 722 mathematics manuscripts from an unreleased internal model
On 6 October 2026 OpenAI published 722 mathematics manuscripts grouped into 372 result families, produced mostly by an unreleased internal frontier model; some proofs have Lean formalizations, while others remain unverified.
ChatGPT contributes most proof ideas in a proposed proof of the KLS conjecture
A 4 October 2026 arXiv preprint presents a proof of the Kannan–Lovász–Simonovits conjecture and says most proofs and mathematical ideas were found by ChatGPT; the authors supplied the suspension idea and improved the exposition.
OpenAI is facing a lawsuit after a hack carried out by an AI agent - صوت الإمارات
OpenAI is facing a lawsuit after a hack carried out by an AI agent صوت الإمارات
Retraction Note: Ribosomal frameshifting in the <i>CCR5</i> mRNA is regulated by miRNAs and the NMD pathway
Retraction Note: Ribosomal frameshifting in the <i>CCR5</i> mRNA is regulated by miRNAs and the NMD pathway
Sign of the times: AI lingo muscles into esteemed dictionary
Sign of the times: AI lingo muscles into esteemed dictionary
2D semiconductor aims to turbocharge development of high-performance electronics
2D semiconductor aims to turbocharge development of high-performance electronics
This award-winning microscopy image used AI — igniting controversy in a prestigious competition
This award-winning microscopy image used AI — igniting controversy in a prestigious competition
Histone readers MLLT1 and MLLT3 concentrate AID to confer locus specificity
Histone readers MLLT1 and MLLT3 concentrate AID to confer locus specificity
OpenAI's research chief talks of ‘cultural reset’ after wild few weeks
OpenAI's research chief talks of ‘cultural reset’ after wild few weeks
AlphaFold 'goes viral': database adds protein complexes of common viruses
AlphaFold 'goes viral': database adds protein complexes of common viruses
Phase Separation Potential of Marsupial RSX RNA Reveals Convergent Evolution of X-Chromosome Inactivation Mechanisms
Background: X-chromosome inactivation (XCI) evolved independently in eutherian and marsupial mammals, where it is orchestrated by the unrelated long non-coding RNAs Xist and RSX, respectively. Xist organizes a repressive nuclear compartment through multivalent RNA-protein interactions, but whether RSX exploits similar biophysical principles remains unknown. A recent paper has identified bona fide RSX interacting proteins. Results: We integrated proteome-scale RNA-protein interaction prediction, experimental validation, phase-separation propensity analysis, functional annotation and comparative
STAT+: Indonesian ruling against ‘patent evergreening’ by pharma industry sends critical message, patient advocates say
A recent court decision in Indonesia sends an important message to other countries to challenge the pharma industry's use of "patent evergreening," patient advocates say.
Ringg’s AI agents resolve up to 65% of customer calls with OpenAI
Using GPT-5.6, Ringg powers multilingual agents across voice, chat, WhatsApp, and web for 90% less cost vs. GPT-4.1.
STAT+: Momentum is building for a big change to how doctors get paid
Congress is considering linking doctor payment rates to medical inflation under the proposed Patients First Act.
vardictcpp: A C++ reimplementation of VarDict variant caller
Genomic variant calling is a central computational task in disease research and diagnostics, where somatic mutations such as JAK2 V617F serve as diagnostic markers in myeloid neoplasms. VarDict, originally written in Perl, and its reimplementation VarDictJava are widely used variant callers in the bioinformatics community. The Perl implementation requires run times of up to several hours; VarDictJava reduces this to a few minutes but can consume large amounts of main memory, which limits the number of jobs that can be run in parallel on a given machine. We present vardictcpp, a functionally id
Australia PM Albanese says OpenAI agent breached government website in June - Reuters
Australia PM Albanese says OpenAI agent breached government website in June Reuters
AI system helps lab devices ‘talk’ with each other — streamlining research
AI system helps lab devices ‘talk’ with each other — streamlining research
The FDA’s Overton era, and dispatches from Shanghai
The Readout LOUD is going international this week with a dispatch from Shanghai, along with notes on the FDA's Overton era.
STAT+: Drugmakers, biotech VCs show ‘reinvigorated’ interest in osteoporosis treatments
Medicines for osteoporosis are grabbing the attention of the pharma industry, which has been incentivized by a long-awaited regulatory change.
Sam Altman’s remarks at the United Nations Security Council
OpenAI CEO Sam Altman discusses AI safety, human control, and international cooperation in remarks to the United Nations Security Council.
Coevolutionary mining of prokaryotic non-coding elements with a genome language model
Microbial genomes encode compact molecular machines and diverse non-coding RNAs (ncRNAs) essential to gene regulation, pathogenesis, and many foundational biotechnologies. However, annotation remains largely protein-centric and homology-driven. Here, we introduce Minerva, a framework for coevolutionary mining that uses genome language models to predict local interactions directly from sequence as two-dimensional maps. Introducing two complementary techniques, categorical Jacobian fingerprinting and interaction heads, we demonstrate fast, accurate, alignment-free prediction of ncRNA base-pairin
White House plan to increase control of NIH grants appears ‘dead’
A planned EO that would have greatly enhanced White House influence over grants at the NIH is “looking dead,” a source said.
A general route to volumetric segmentation under annotation scarcity
Volumetric cell segmentation is limited by the scarcity of labelled data, because manual curation of three-dimensional datasets is prohibitively laborious. We find that this gap can be bridged with volumes in which synthetic 3D objects mirror the properties of real cells and of image formation, notably the axial spread of the point-spread function. Such volumes provide unlimited labelled training data, and we show that a model trained on them produces accurate segmentations of real biological data.
OpenAI extends cyber access to Ukraine for civilian defense
OpenAI is extending access to its Daybreak program to the Government of Ukraine to support the cyber defense of civilian infrastructure.
Lean Pool: An AI-Maintained Archive of Formalized Mathematics
arXiv:2609.25199v1 Announce Type: new Abstract: Lean Pool is a repository of formalized mathematics. It is grown, maintained and optimized by AI agents.
Chinese-speaking hacker used AI agents to steal 600,000 credit cards - qz.com
Chinese-speaking hacker used AI agents to steal 600,000 credit cards qz.com
Comparative analysis of 15 chromosome-scale near T2T assemblies of Brazilian Fusarium graminearum isolates
Fusarium graminearum is the causative agent of Fusarium head blight of wheat, yet genomic resources for Brazilian populations, where it is the dominant species, remain limited to a single reference genome. We report fifteen near telomere-to-telomere (T2T) assemblies of Brazilian F. graminearum isolates generated with Oxford Nanopore sequencing. Each genome was assembled into four nuclear chromosomes, with 118 of 120 telomeres resolved, BUSCO completeness of 99.199.3%, and 14,80214,894 predicted genes per isolate. The genomes are structurally conserved, showing no chromosome-scale rearrangement
STAT+: Flagship gets a Pentagon contract
Biotech investment firm Flagship Pioneering has been awarded a contract by the Department of Defense to advance countermeasure technologies against biological threats.
Advancing Private AI Compute with secure, server-side memory
Introducing private, server-side memory to Private AI Compute for personal AI.
The latest in the legal saga for youth gender-affirming care in the U.S.
The chilling effect of the Trump administration's dogged offensive against transgender health care has been swift, with dozens of hospitals across the U.S. ending care, even if they haven't been…
DNA analysis strengthens evidence that Thomas Jefferson fathered children with Sally Hemings
Researchers sequenced president’s genome from a lock of hair taken shortly after his death, then compared his DNA with that of Hemings’s living descendants
Lawsuit demands OpenAI pay for new school after ChatGPT used in shooting - Ars Technica
Lawsuit demands OpenAI pay for new school after ChatGPT used in shooting Ars Technica
STAT+: Why an influential GOP health policy expert still backs the Medicaid cuts — and which policy target is next
Why an influential GOP health policy expert still backs $1 trillion in Medicaid cuts — and which policy target is next.
Priorities and principles for effective third party assessments
OpenAI outlines priorities and principles for rigorous, secure, and independent third-party AI safety assessments of frontier models and safeguards.
CTSpinoPelvic1K: spine, pelvis, ribs and femora in one coordinate frame, annotated for lumbosacral transitional anatomy
arXiv:2609.22760v1 Announce Type: new Abstract: Purpose: A vertebra at the lumbosacral junction is named by counting caudally from C2 on whole-spine imaging, but a lumbar case is planned on lumbar-only imaging (T12 to S1), without C2. Abdominopelvic CT holds that span plus the lowest ribs and pelvis. Where a lumbosacral transitional vertebra (LSTV) alters the count, the local anatomy is ambiguous: four rib-free vertebrae may be an L1 with a lumbar rib or an L5 assimilated to the sacrum, and six may be a sixth lumbar vertebra, a T12 with aplastic ribs, or a lumbarized S1. CTSpinoPelvic1K asks
Higgsfield AI ships new video features in a day with GPT-6 Astra
With GPT-6 Astra, Higgsfield AI makes video ad creation easier for small businesses and brings new creative tools to market faster.
A single-cell RNA-seq catalog of ground truth gene coregulation
Single-cell RNA-sequencing measurements are uniquely well-poised to identify coregulated gene transcription. Coregulation should be apparent as correlation of expression levels, but at which unit to quantify expression for calculation of correlations is not clear. To enable evaluation of normalization methods for identification of coregulation we have curated and preprocessed 35 scRNA-seq samples with known regulature relationships into a catalog. These datasets contain promoter-reporter data representing known positive coregulation and B cells where isotypic mutual exclusion of {kappa} and {l
celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool
Motivation Single-cell RNA sequencing (scRNA-seq) cluster annotation is a critical step in data analysis. Current methods are time-consuming, difficult to reproduce, or limited in tissue or species coverage. Results We developed celltypeEnrich, a cluster-level annotation tool that uses a hypergeometric test to identify enrichment of cell-type-specific genes from input gene lists. Enrichment results from up to 26 reference datasets are used to determine a consensus annotation. Benchmarking using scRNA-seq datasets from three tissues spanning two species showed 62-72% annotation accuracy for cel
Reaction-aware hypergraph learning of chemical representations with LARK
Molecular pretraining offers a route to learning transferable chemical representations from unlabelled data. However, existing approaches pretrained on isolated molecular structures struggle to generalize to reaction-specific tasks because their pretraining objectives provide limited information about chemical transformations. A key challenge is to integrate this reaction information into chemical representations. Here we introduce LARK, a unified framework for learning atom-molecule-reaction knowledge from chemical transformations. Using approximately 0.6 million reactions encompassing 0.88 m
British Columbia Sues OpenAI for Failure to Warn Before Shooting - Bloomberg.com
British Columbia Sues OpenAI for Failure to Warn Before Shooting Bloomberg.com
Skeptical about modern medicine? I know the feeling — and want to listen
"I know what it means to be searching for an explanation to what ails you, and a way to fix it," writes STAT's new trust-in-science reporter, Nicholas Florko.
Vritra: gene-specific reference construction for species-resolved functional profiling of metagenomic and metatranscriptomic data
Species-resolved profiling of microbial functional genes is important for linking microbial community composition to biological function, but existing functional profiling approaches are not designed to systematically define user-specified genes and resolve their contributing species. We developed Vritra (Versatile gene-guided Reads-identification with Impartial Taxonomic Refinement and Assignment), a framework for constructing gene-specific reference databases for metagenomic and metatranscriptomic data. Vritra expands and refines the UniRef-based sequence space for user-specified target gene
Daily briefing: The first new cat species described by science in a century
Daily briefing: The first new cat species described by science in a century
stEDGE enables edge-guided multiscale reconstruction of hierarchical spatial domains and transition interfaces in spatial transcriptomics
Spatial transcriptomic technologies enable high-resolution mapping of tissue architecture, yet most computational methods still represent tissues as flat spatial partitions, limiting their ability to resolve spatial domain boundaries, gradual transition regions and nested hierarchies. Here we present stEDGE, an edge-guided and interpretable framework for multiscale reconstruction of spatial domain hierarchies and transition-associated states in spatial transcriptomics. Unlike conventional domain-first approaches, stEDGE first estimates local boundary structure and uses it to guide the reconstr
Pitfalls in understanding PhIP-Seq data: technical variability, replicability, and best practices for interpretation
Phage Display Immunoprecipitation sequencing (PhIP-Seq) is a high throughput method allowing to measure antibody binding against hundreds of thousands of potential antigens. Typically, blood samples of hundreds to thousands of individuals are measured in 96-well plates, necessitating distribution of samples on multiple plates and processing in batches. To correctly interpret PhIP-Seq results, it is crucial to understand its nature and technical limitations. Here, we analyzed 195 technical replicates of controls (anchor samples) from 49 plates measured in 13 immunoprecipitation runs to gauge te
AI co-scientists are revolutionizing how research is done
AI co-scientists are revolutionizing how research is done
A transcriptional continuum from clinically normal to lesional skin: Single-cell trajectory analysis of psoriasis
Psoriasis is a chronic inflammatory skin disease characterized by immune dysregulation and complex cellular interactions involving keratinocytes (KCs), T cells, and endothelial cells. Single-cell studies have mapped the cellular diversity of psoriatic plaques, yet most analyses treat skin as either healthy or diseased and therefore overlook the graded changes that precede a visible lesion. We asked whether psoriasis instead progresses along a transcriptional continuum and whether clinically normal skin from patients already carries a measurable disease signature. Using single-cell RNA sequenci
STAT+: What a vote on Grail’s cancer detection test could mean for the company, patients, and regulators
Shares in Grail, the company developing a blood test for cancer, surged Monday after the FDA posted documents ahead of an advisory committee meeting.
Development and evaluation of a core genome multi-locus sequence typing scheme for the Enterobacter cloacae complex
The Enterobacter cloacae species complex (ECC) comprises a group of closely related, opportunistic Gram negative bacteria of major public health concern due to their frequent involvement in healthcare-associated infections and their capacity to acquire and disseminate multidrug resistance, including carbapenemases. Because members of this complex are often difficult to distinguish phenotypically and their taxonomic status is subject to debate, there is a need for a standardized, species-complex wide typing scheme based on whole genome sequencing (WGS) that can be applied to any species within
Measurement reliability bounds functional benchmarks and relocates where variant effect prediction fails
Background. Variant effect predictors are increasingly benchmarked against multiplexed assays of variant effect (MAVEs) rather than clinical labels, which removes label circularity but introduces a new problem: a correlation against a measurement cannot exceed the measurement's own reproducibility, and precision varies sharply across the territories compared. Results. We scored nineteen predictors across sixteen strata of a frozen atlas of 64,178 saturation genome editing variants in seven cancer-susceptibility genes. From published replicate scores and standard errors we estimated each territ
Structure-based Antibody Renumbering
Antibody numbering schemes like IMGT and Chothia assign each residue in the variable domain a consistent index based on substructural position. These annotations standardize sequences with different lengths, facilitating tasks ranging from engineering of individual molecules during drug development to large-scale curation of training data for de novo antibody design. Yet existing algorithms for performing this annotation process, termed renumbering, rely exclusively on amino acid sequence for inference. Consequently, these can fail when presented with unnatural or unusual features such as long
CUBE: Multimodal Representation Learning Reveals Biological Structure Across Histomorphology, Spatial Protein Phenotypes, and Transcriptome-Associated Signals
Integrating histological, spatial protein, and transcriptomic information into a biologically grounded representation remains challenging because these modalities are rarely available as fully paired measurements, while existing computational approaches are commonly developed around individual modality pairs. To address this, we present CUBE (Colorectal Universal Representation & Bridge Encoder), a multimodal representation-learning framework that uses hematoxylin and eosin (H&E) histology as a bridge to integrate spatial protein phenotypes with transcriptome-associated information from incomp
Scaling Network Medicine with LLMs for Combinatorial Drug Repurposing in ER+ Breast Cancer
Drug repurposing can accelerate therapy discovery for ER+ breast cancer, but combination selection remains difficult. We developed an LLM-driven network medicine framework that extracts drug--target relationships from 595,122 PubMed abstracts, builds a cross-model consensus network, overlays it onto the KEGG estrogen signaling pathway, and enumerates complementary drug pairs. Candidate combinations are ranked by ComboRank, which aggregates pathway coverage, LLM consensus, RAG validation, cross-method agreement, and ClinicalTrials.gov precedent. The framework identified 166 significant pairs at
BELL: Biomodel Evidence and LLM-based Logic
Building accurate and predictive mechanistic models requires careful biological interaction curation and verification against existing knowledge. When done manually, these tasks become impractical, especially with massive extraction of interactions facilitated by advanced natural language processing methods and large language models (LLMs). We present BELL (Biomodel Evidence and LLM-based Logic), a biocuration support framework that automates evidence retrieval, scoring, and explanation for interaction-level verification. BELL processes each interaction through a five-step pipeline: entity gro
miRstring: An RNA language model enables mature miRNA decoding and artificial small RNA design across species
MicroRNAs (miRNAs) are processed from structured precursors and subsequently loaded into Argonaute proteins to repress target mRNAs. Yet generalized computational frameworks capable of decoding mature miRNAs from precursor context remain limited, constraining both cross-species annotation and rational artificial miRNA design. Here, we present miRstring, a biogenesis-aware RNA language framework that decodes the four boundaries defining the miRNA/miRNA duplex. Trained on 77,708 miRNA precursors spanning 414 species, miRstring outperforms existing methods under family- and species-held-out evalu
Multi-model biological and sequence information fusion for gene regulatory network inference from single-cell transcriptomics
Identification of transcription factor target gene interactions and construction of the gene regulatory networks (GRNs) are essential for understanding the molecular mechanisms underlying transcriptional gene regulation. Large scale single cell transcriptomics across different tissues offers unprecedented resolution of cellular diversity and regulatory dynamics by capturing gene expression heterogeneity. However, existing methods often lack effective multimodal integration and fail to fully exploit the hierarchical structure in Gene Ontology (GO) and gene sequence level representations, which
Google’s Gemini AI hacks 3 companies in security test, then stops - Al Jazeera
Google’s Gemini AI hacks 3 companies in security test, then stops Al Jazeera
Somatic haplotype reconstruction and variant recalibration from tumor-only long-read sequencing
Separating somatic from germline variants and reconstructing somatic haplotypes are the two central problems of tumor-only cancer genome analysis. Long reads carry the linkage needed to solve both, but chromosome-scale loss of heterozygosity (LOH) and an unknown degree of normal-cell admixture blur the distinction between somatic and germline haplotypes. Here we present LongPhase-TO, the first method to reconstruct somatic haplotypes from a tumor sample alone. Rather than mapping somatic variants onto germline haplotypes, LongPhase-TO co-phases germline and somatic alleles in a unified graph,
Comparative Mitogenomics and Molecular Phylogeny of Agriculturally Significant Tephritid Fruit Fly Pests in Bangladesh
Tephritid fruit flies of the tribe Dacini rank among the most economically destructive agricultural pests globally, with several Bactrocera Macquart and Zeugodacus Hendel species causing severe losses to fruit and vegetable production across South and Southeast Asia. Bangladesh harbors five dacine species of primary agricultural significance: Bactrocera dorsalis (Hendel), B. carambolae Drew & Hancock, B. zonata (Saunders), B. correcta (Bezzi), and Zeugodacus cucurbitae (Coquillett). Here we present a comparative mitogenomic and molecular phylogenetic framework based on 21 unique mitogenome rec
QVAC Genesis III: A Large-Scale, High-Quality Open Synthetic STEM Corpus for Efficient Language Model Pre-Training
arXiv:2609.19513v1 Announce Type: new Abstract: High-quality pre-training data is a critical bottleneck for educational and STEM-specific language models targeting edge AI and on-device deployment where token budgets are tightly constrained. While major organizations train ever-larger models on private corpora, the open ecosystem lacks STEM-focused synthetic datasets that deliver high per-token learning value efficiently for small models. To address this gap, we introduce QVAC Genesis III, a 191.43B-token, STEM-focused multi-domain synthetic corpus covering 19 domains across several difficult
Agentic AI Networking for Heterogeneous Unmanned Aerial Systems in Low-Altitude Wireless Networks
arXiv:2609.19538v1 Announce Type: new Abstract: Low-altitude wireless networks (LAWNs) are emerging as a key infrastructure for heterogeneous unmanned aerial systems that support concurrent services within a shared three-dimensional airspace. Their coexistence creates strong coupling among mobility, connectivity, and shared network resources, while heterogeneous services impose distinct and time-varying requirements. These interactions naturally form a dynamic non-cooperative game in which both operating conditions and coordination objectives evolve over time. Conventional optimization and le
What Do Current Systematic Generalization Tasks Miss? A Reasoning-Centered Analysis
arXiv:2609.19212v1 Announce Type: new Abstract: Systematic generalization, the ability to solve novel problems by recombining known atomic elements, is central to human intelligence but difficult to study rigorously under controlled settings. Existing studies therefore rely on simplifications such as approximately linear action composition, productivity-based tests, and action-explicit goals, which make systematic generalization easier to study but omit some essential aspects of this capability. To characterize what these simplifications miss, we adopt a reasoning-centered lens and introduce
Closed-World Resolution Against Tool Hallucination in LLM Agents
arXiv:2609.19425v1 Announce Type: new Abstract: Tool-augmented large language model (LLM) agents fail in a way no tool-selection or tool-security method addresses: they call tools that do not exist and pass arguments no schema declares. Existing defenses either pick the right tool (selection) or constrain what an agent may do with real tools (gating), both of which presuppose the emitted call refers to a real tool at all. We show this is a structural blind spot: a hallucinated call is by construction not a decision any gate made, so no gate can reject it. This paper is primarily a measurement
The syntax and semantics of goals
arXiv:2609.19448v1 Announce Type: new Abstract: In both cognitive science and computer science, goals are conceptualized as cognitive states that flexibly combine with world knowledge to organize and specify purposeful behavior. In this way, goals are compositional representations whose content relates to rational behavior. We here draw attention to goals as representations and their content because it highlights a parallel with other areas in cognitive science - in particular, the syntax-semantics interface in linguistics and logic - while also foregrounding foundational questions about the
Department of Energy launches $215 million race to build quantum computer by 2028
Agency aims to select teams for milestone-driven competition by year’s end
Position: It is Time to Virtualize Foundation Models with a Self-evolving Operating System Layer
arXiv:2609.19203v1 Announce Type: new Abstract: AI applications have shifted from single, monolithic foundation models (FM) to compound agentic systems. Yet today's stacks remain fragmented: even as protocols (e.g., MCP, A2A) ease tool/agent connectivity, each framework embeds an implicit runtime for state, memory, budgets, and guardrails, making behavior non-portable and governance brittle. It mirrors computing before operating systems, when every program re-implemented basic services. This position paper argues that the field now needs a Foundation Model Operating System (FMOS) -- a system
STAT+: In a fight over denied claims, HCA sues out-of-state Blue Cross Blue Shield plan
Several Texas hospitals are suing an out-of-state Blue Cross for unpaid medical claims, highlighting the varying standards for whether care is medically necessary.
Security Researchers Hacked Into OpenAI Using Anthropic’s Claude - Forbes
Security Researchers Hacked Into OpenAI Using Anthropic’s Claude Forbes
Accelerating antibiotic discovery with ChatGPT - OpenAI
Accelerating antibiotic discovery with ChatGPT OpenAI
From Intent to Action: Benchmarking LLM Safety in Vehicle Voice Command Authorization
arXiv:2609.19630v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into vehicle voice assistants. But linking natural-language requests to vehicle functions creates a safety-critical authorization problem. Before executing a command, the system must choose whether to execute, refuse, clarify, require confirmation, defer to manual control, trigger an emergency response, or make no tool call. To our knowledge, prior evaluations do not isolate this pre-action decision across speaker role, authentication status, vehicle state, and tool availability. We introd
STAT+: Trump touts pharma deals to offer cheaper drugs to state Medicaid programs
Every state will participate in the GENEROUS program, but many of the details are unknown.
Introducing the Australian Youth Safety Blueprint
OpenAI introduces the Australian Youth Safety Blueprint, a six-pillar roadmap for safer AI experiences that protect and empower young people.
Regularized Emphatic Temporal-Difference Learning: Stability under Constant Stepsizes
arXiv:2609.19170v1 Announce Type: new Abstract: Emphatic temporal-difference learning (ETD) stabilizes the expected off-policy TD update and changes its projection geometry, but neither property determines constant-stepsize sampled dynamics. We construct an ergodic two-state counterexample in which the ETD mean map contracts while the sampled product has a positive top Lyapunov exponent. Regenerative-cycle analysis separates this sign from the infinite variance of the follow-on trace. We introduce regularized emphatic TD (RETD), a normalized first-order post-shock repair that leaves the trace
Setting the SCENE for Interpretable Cell-Gene Embeddings in Single-Cell RNA-seq
Single-cell RNA sequencing measures cellular states at high resolution, but sparse high-dimensional count data remain difficult to model interpretably. We introduce the Single-Cell Euclidean Network Embedding (SCENE), a probabilistic latent-distance model that jointly embeds cells and genes from Unique Molecular Identifier (UMI) counts. SCENE treats the count matrix as a weighted bipartite cell-gene graph, where Euclidean distances represent transcriptional affinity, and combines this geometry with a zero-inflated count likelihood that separates gene detection from expression magnitude. Across
Novel two-stage deep learning-based approach applied to gene expression data pertaining to esophageal adenocarcinoma boosting biological knowledge discovery
Esophageal cancer (EC) is characterized by complex transcriptional alterations and therapeutic resistance, posing challenges for traditional computational methods. In this study, we propose a deep learning (DL)-based computational framework to identify important genes and biologically relevant pathways in bulk cell RNA-seq data (GSE234304 and GSE273848), which comprise tumor and non-tumor esophageal tissue samples. A fully connected feedforward neural network was trained for binary classification, and two feature selection strategies were implemented: Neural Network followed by Support Vector
Anthropic Quietly Builds Wet Lab, Pushing AI Drug Discovery Ambitions from Simulation into the Physical World - finance.biggo.com
Anthropic Quietly Builds Wet Lab, Pushing AI Drug Discovery Ambitions from Simulation into the Physical World finance.biggo.com
How to make a brain: new experiments challenge existing picture
How to make a brain: new experiments challenge existing picture
Novo Nordisk taps Anthropic's Claude to speed drug discovery - Drug Discovery News
Novo Nordisk taps Anthropic's Claude to speed drug discovery Drug Discovery News
OpenAI maths bombshell sparks debate about who gets credit in age of AI
OpenAI maths bombshell sparks debate about who gets credit in age of AI
Physics-Constrained Digital Twins for Sensor Integrity in Urban Pedestrian Flow: Detecting Stealthy False Data Injection with Conformal Guarantees
arXiv:2609.17635v1 Announce Type: new Abstract: City pedestrian counting systems now feed economic indicators, planning decisions and safety operations, yet the twins built on top of them treat the incoming stream as ground truth. We study what happens when it is not. We formalise stealthy false data injection for city-scale pedestrian sensing, where the map from latent flow to observation is far more rank deficient than in the power and water networks for which stealth has been characterised. Our twin estimates directed flows on the pedestrian street graph, assimilates counts through a learn
How Anthropic & Novo Will Use Claude AI for Drug Discovery - AI Magazine
How Anthropic & Novo Will Use Claude AI for Drug Discovery AI Magazine
GraphEcho: Structural Redundancy and Evidence Provenance in LLM Graph Agents
arXiv:2609.17695v1 Announce Type: new Abstract: A large language model (LLM) agent can follow more graph paths without acquiring more independent evidence. GraphEcho tests whether agents mistake these repeated encounters for additional corroboration. The benchmark varies path counts and evidential origins while holding evidence content fixed, and evaluates both judgments and active exploration. Controlled synthetic experiments reveal model-dependent judgment shifts, but redundant supporting paths increase the share of repeated walks across all evaluated frozen agents. Provenance-aware post-tr
ERPBench: A State-Grounded Evaluation Paradigm for Computer-Use Agents in Enterprise Software
arXiv:2609.17885v1 Announce Type: new Abstract: Computer-use agents that operate through screenshots and simulated actions are advancing rapidly, yet their evaluation remains anchored to general desktop and web tasks. Enterprise Resource Planning (ERP) systems run the finance, procurement, inventory, and customer operations of organizations worldwide, and pose distinct challenges for computer-use agents: dense interfaces, coordinated multi-step interactions, and errors that alter persistent business records rather than surfacing on screen. Existing enterprise benchmarks rely on proprietary pl
FairCompressAgent: An Agentic Framework for Fairness-Aware Model Compression for FPGA Deployment
arXiv:2609.17786v1 Announce Type: new Abstract: Fairness-aware model compression requires selecting methods and configurations that balance accuracy, fairness, and deployment cost. These decisions become more difficult when compression methods are composed or the user's requirements change. In this paper, we propose FairCompressAgent (FCA), an agentic framework that integrates fairness-aware pruning, incremental quantization, and sparse low-rank factorization through a common operator interface. A language-model planner uses model profiles and measured outcomes to select compression configura
Collaborative Memory for Multi-Agent VLM Systems
arXiv:2609.17921v1 Announce Type: new Abstract: Vision-language model (VLM) agents combine specialized perception, tools, and reasoning to address complex visual tasks. In multi-agent settings, different agents inspect different image regions, video frames, or visual representations, so collaboration extends beyond distributed reasoning to distributed perception. This makes shared visual context a central problem in VLM agent collaboration. In this paper, we frame memory hierarchy, cross-agent sharing, and consistency mechanisms around the need to reconcile interpretations and update dependen
Integrating Genomic Annotations and Traits Dependencies for single-nucleotide polymorphisms Prioritization with Causal Concept Bottleneck Models
Predicting common traits from single-nucleotide polymorphism (SNPs) data is challenging due to polygenicity, small effect sizes, and the presence of potentially mediated or spurious cross-trait associations. We propose a modeling approach that combines genomic annotations with known cross-trait relations by leveraging Causally Reliable Concept Bottleneck Models (C2BM), a deep learning architecture that factors the joint trait distribution over a graph of interpretable concepts. This design allows trait predictions to leverage information from other observed traits in addition to genomic inputs
OpenAI’s Rogue Agents Probed Hugging Face Two Months Before the July Breach - Technology Org
OpenAI’s Rogue Agents Probed Hugging Face Two Months Before the July Breach Technology Org
Introducing Astra for Law
OpenAI for Law brings frontier intelligence for law, custom firm workflows, connected legal data sources, and legal-grade controls for confidential client work.
NeMo Data Designer: An Extensible Framework for Multimodal Synthetic Data Generation
arXiv:2609.17699v1 Announce Type: new Abstract: We present NeMo Data Designer (NDD), an open-source, general-purpose framework for multi-modal synthetic data generation (SDG). Designed to be intuitive to use, NDD provides a declarative configuration format in which human and/or agent users define each dataset column, with column types spanning text, code, structured outputs, images, embeddings, and statistical samplers that are explicitly configured to steer dataset diversity. Additional column types and functionality can be introduced using the framework's flexible plugin system. NDD's confi
A Four-Stage Decomposition of Word-Problem Solving and Mechanistic Fragility in LLM Math Reasoning
arXiv:2609.17804v1 Announce Type: new Abstract: Large language models solve grade-school math word problems with high accuracy, yet a single irrelevant clause inserted into the problem can collapse it. We reconcile these observations with a mechanistic account. We show that the model's internal computation decomposes into a four-stage sequential pipeline, Schema Abstraction, Operation Planning, Operand Binding, and Computation, each stage producing a distinct intermediate representation in an identifiable band of layers. Using the same scaffold to diagnose distractor-induced failure, we local
How a team of AIs discovered a promising lung-cancer drug
How a team of AIs discovered a promising lung-cancer drug
Integrating structural and biological evidence to rerank ESMFold2 protein-protein interactions
Large-scale protein structure prediction enables proteome-wide protein-protein interaction (PPI) screening, but distinguishing biologically meaningful interactions from spurious interfaces remains challenging. Here, we develop a scalable framework combining fast, MSA-free ESMFold2 prediction with PAE-guided domain parsing and evidence-based reranking. Three-recycle ESMFold2-Fast achieved 57% acceptable-or-better DockQ scores on FoldBench, comparable to AlphaFold2-Multimer while substantially reducing computation. PAE-guided parsing preserved 98.1% of XL-MS cross-links within parsed domain pair
OpenAI Agents Were Using Messaging Prior To Hugging Face Incident - Forbes
OpenAI Agents Were Using Messaging Prior To Hugging Face Incident Forbes
CapMem: A Benchmark for Caption-Based Episodic Memory in Egocentric Video
arXiv:2609.17688v1 Announce Type: new Abstract: Wearable assistants require episodic memory over egocentric video, yet current vision-language models face bounded frame budgets, growing visual-token costs, and long-context retrieval failures. Under these practical constraints, we study whether textual captions can serve as reusable episodic memory. We define the Episodic Memory Video Caption QA task and introduce CapMem, a human-annotated benchmark with 75 videos totaling 33.7 hours, and 1,000 multiple-choice questions across 16 scenarios. On long videos (>20 min), full-coverage CaptionQA wit
Novo to partner with Anthropic on drug discovery efforts - Yahoo Finance
Novo to partner with Anthropic on drug discovery efforts Yahoo Finance
OpenAI Hugging Face incident began two weeks earlier than disclosed, research claims - computing.co.uk
OpenAI Hugging Face incident began two weeks earlier than disclosed, research claims computing.co.uk
Novo taps Anthropic's Claude AI for drug discovery - pharmaphorum
Novo taps Anthropic's Claude AI for drug discovery pharmaphorum
From Prompt to Pipeline: A Comparative Evaluation of Large Language Model Coding Agents for Reproducible Bioinformatics Pipeline Construction
Agentic coding systems are increasingly presented as a way to reduce the engineering burden of scientific software development. Bioinformatics is a strong test case for this claim because useful pipelines must combine domain-specific analysis choices, command-line software, sample metadata, workflow orchestration, container or HPC execution, and interpretable quality-control reporting. We evaluated three agentic systems - Biomni, Claude Code, and Codex - on the same task: constructing a Nextflow DSL2 pipeline for paired-end CUT&Tag data that included read QC, trimming, alignment, filtering, du
How fast are you ageing? Ask AI
How fast are you ageing? Ask AI
Anthropic's Drug Discovery Partners: AI Pharmaceutical Orders Set to Double Next Year - 36Kr
Anthropic's Drug Discovery Partners: AI Pharmaceutical Orders Set to Double Next Year 36Kr
Novo and Anthropic Partner to Apply Claude to Drug Discovery - HLTH
Novo and Anthropic Partner to Apply Claude to Drug Discovery HLTH
Take a risk or play it safe? Neuronal tug-of-war helps the brain decide
Take a risk or play it safe? Neuronal tug-of-war helps the brain decide
What You Can't See Is Still What You Learn: A Preregistered Sixty-Society Confirmation That Evidence Masking Drives Compositional Generalization
arXiv:2609.17637v1 Announce Type: new Abstract: Restricting what a module can read may improve what a system learns to compute. We test this in a preregistered confirmation with sixty four-cell systems sharing a frozen language-model backbone and communicating through learned continuous packets. Five conditions vary evidence masking, ownership markers, and replacement of foreign evidence with neutral filler, across six initialization clusters, each with two data orders, on one fresh task world. With markers available in both regimes, masking improved accuracy on held-out two- and three-operat
STAT+: A Sellas immunotherapy study, long delayed, will end in failure
Why Sellas' experimental immunotherapy study is a blowup waiting to happen.
RNAbridge: a database of extended and non-canonical helices
RNA function is dictated by 3D architecture. Although 2D structural models based on canonical Watson-Crick base pairs are widely used, they often fail to capture the non-canonical interactions, tertiary contacts, and coaxial stacking important for biological activity. We have developed RNAbridge, a comprehensive database and web application that systematically identifies, quantifies, and visualizes extended non-canonical helices and multi-way junctions. Using a geometry- and stacking-based pipeline, we analyzed the Protein Data Bank and compiled a catalog of 135,541 structural motifs. RNAbridg
STAT+: Senators highlight the power Chris Klomp wields at confirmation hearings
Chris Klomp has already played a major role in the Trump administration and its Make America Healthy Again agenda.
Reimagining advertising with AI
Explore new AI-powered advertising experiences from OpenAI, including Sponsored Agents, tools for marketers, and integrations with HubSpot and Shopify.
OpenAI AI Agents Probed Hugging Face Before July Breach - StratNews Global
OpenAI AI Agents Probed Hugging Face Before July Breach StratNews Global
Reimagining research papers as interactive and reliable AI agents
Reimagining research papers as interactive and reliable AI agents
Mutational constraints on RSV F and its neutralization by antibodies
Mutational constraints on RSV F and its neutralization by antibodies
STAT+: Scientists made mice with half a brain. Then they filled them with human organoids
Scientists have made mice with half a brain, then filled them with human organoids — possibly opening new avenues for research but also raising ethical issues.
stably: error-controlled stability selection for biomarker panel discovery
Motivation Data-independent acquisition mass spectrometry (DIA-MS) has become increasingly popular for clinical proteomics due to its sensitivity and reproducibility. Univariate statistical analysis tools, such as limma and MSstats, are widely used for identifying differentially abundant proteins but cannot capture multivariate relationships between proteins that may provide greater discriminatory power as a panel. Machine learning approaches can address this gap, but typically prioritise predictive performance over feature stability, producing biomarker panels for downstream validation that v
STAT+: GOP aims to change Medicare adviser’s math so it’s more favorable to health insurers
The GOP bill directs MedPAC to study the topic of overpayments to Medicare Advantage plans in a manner that’s more favorable to insurers.
OpenAI's rogue agents were probing Hugging Face for weaknesses months before the breach - qz.com
OpenAI's rogue agents were probing Hugging Face for weaknesses months before the breach qz.com
Novo and Anthropic will collaborate to advance drug discovery with Claude - Yahoo Finance
Novo and Anthropic will collaborate to advance drug discovery with Claude Yahoo Finance
Fine-Tuning Fixes Mode Collapse and Over-Dispersion in LLMs
arXiv:2609.16454v1 Announce Type: new Abstract: Recent work by Doshi and Hauser (2024), Bisbee et al. (2024), and Xie et al. (2026) raises concerns that outputs from large language models (LLMs) tend to be under-diverse: they repeat or resemble one another more often than responses from the population they are meant to represent, a phenomenon known as mode collapse. In this work, we show that whether mode-collapse, or its opposite, occurs depends on the specific model and dataset used. Further, with sufficient supervised fine-tuning (SFT) data, LLM output diversity converges toward that of th
Opinion: How my cancer diagnosis changed the way I train physicians
“The tumors, test results, and treatments I present to future physicians? Mine,” writes David Elkowitz, a professor of pathology and laboratory medicine and science education.
Opinion: STAT+: Dementia should be a campaign issue — not an insult
STAT’s Neurotransmissions columnist Jason Karlawish asks: What are the harms of the way politicians talk about dementia? What should they say instead?
OpenAI discloses six new AI safety incidents - Axios
OpenAI discloses six new AI safety incidents Axios
Map of brain ‘microproteins’ could offer new clues to Alzheimer’s disease
Map of brain ‘microproteins’ could offer new clues to Alzheimer’s disease
AI companies must work with the research community to protect attribution
AI companies must work with the research community to protect attribution
nanorepertoire: an end-to-end Nextflow pipeline for nanobody repertoire analysis
Camelid heavy-chain antibodies, and particularly their variable domains known as nanobodies or VHHs, combine full antigen-binding capacity with a compact and highly stable scaffold, which makes them attractive for both fundamental immunology and therapeutic development. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) allows nanobody repertoires to be profiled at great depth, but the analyses applied to VHH data are typically assembled ad hoc from standalone scripts, which limits standardisation and reproducibility across laboratories. Here we present nanorepertoire, a
Transplanted human brain-tissue takes root in mice without a cortex
Transplanted human brain-tissue takes root in mice without a cortex
How workers are unlocking new ways of working
New OpenAI Economic Research shows how workers use AI beyond traditional roles and which new activities become recurring parts of their work.
SixPack-AbScan: a web server to discover cross-reactivity of antibodies across species
Availability of commercial antibodies for immunochemistry is typically limited to major model species. Researchers working on non-model organisms therefore often need either to generate novel species-specific antibodies or venture into expensive empirical screening from available antibody catalogues, hoping to find cross-reactive reagents. SixPack-AbScan is a free web server aiding researchers in transferring antibodies across species: using the available epitope-mapping information, the software performs a simple computational pre-screening of potential cross-reactivity. The workflow is speci
Atlas of Proteomic Technologies: an evidence-based framework for selecting and combining commercial proteomics platforms
High-throughput proteomics now spans several technologies that differ in biological breadth, precision, specificity, sensitivity, cost, and method(s) of quantification; however, investigators currently lack an evidence-based framework to select or combine platforms. Here, we present the Atlas of Proteomic Technologies (APT) - a curated resource that evaluates six commercial platforms across ten analytical dimensions using published head-to-head evidence, expert evaluation, and iterative peer review. APT hosts two decision tools: the Help Me Choose tool maps a study's primary aim(s), scale, sam
Flow Orchestrated Regulatory Genomics Engine (FORGE): A Configurable Nextflow Pipeline for End-to-End snMultiome Analysis
MOTIVATION Single-nucleus resolution multiome (snMultiome) assays concurrently profile gene expression and chromatin accessibility in the same nucleus. Yet regulatory inference from such analyses are difficult to scale, audit, and reproduce; moreover, as a field, snMultiomics and its toolset remains far from standardized. To address these challenges we developed FORGE, a configureable Nextflow workflow that carries paired data from raw counts and fragments through regulatory network inference with a comprehensive differential testing suite. Execution is containerized, tracks provenance, robust
CLEAR: Cross-Source Evidence Adjudication for Large Language Models in Medicine
arXiv:2609.16301v1 Announce Type: new Abstract: Medical knowledge evolves continuously, whereas the parametric knowledge encoded in large language models (LLMs) is fixed at training time. External retrieval, including retrieval-augmented generation (RAG), can provide access to newly available evidence, but retrieved information may be irrelevant, incomplete, or conflicting. As a result, external retrieval can in turn degrade the factual accuracy and evidence grounding of LLM outputs. To address this challenge, we propose \textbf{CLEAR}, an agentic framework for cross-source evidence adjudicat
scACORN: Context-engineered agent orchestration of specialized small language models for single-cell transcriptomic interpretation
Single-cell atlases now exceed 66 million cells, but turning a ranked expression profile and a free-form biological question into a reliable, evidence-grounded answer remains unsolved. Scaling a single model does not resolve this, because single-cell interpretation is a heterogeneous family of tasks whose correct answer depends on tissue, cohort, perturbation and annotation resolution. Here we present scACORN, an agentic alternative to monolithic single-cell language models that combines specialized small language models with context-engineered agent orchestration for their selection and compo
Genome-scale perturbation signatures from primary human CD4+ T cells improve genetics-based prioritization of immune drug targets
While human genetic evidence improves drug program success, target selection largely relies on associational features. Even functional features are mainly observational, capturing disease associations rather than the consequence of perturbing genes in the disease-relevant human cell type. To move beyond association, we present IGNITE (Immune Genomics and fuNctional Integration for Target Enrichment), a framework that prioritizes immune drug targets by integrating human genetic priors with features from genome-scale perturb-seq in 22 million primary human CD4+ T cells and polarized T-helper sub
Ourotide: decoding the hierarchical peptide recognition for generative design
The historical dichotomy between small-molecule pocket and extended protein interfaces misrepresents the physical reality of peptide recognition.1,2 Here we show that peptide binding is not a simple structural intermediate but a distinctly multimodal landscape comprising small-molecule-like pockets, protein-like interfaces, and a previously unrecognized third mode. This third regime is governed by a hierarchical subpocket architecture where a flattened surface achieves near-complete peptide engagement through spatially partitioned hydrophobic components. To maintain stability, an incompletely
Rsearch: An R interface to VSEARCH supporting visualization and parameter tuning
Background: We present Rsearch, an R package that integrates the core functionality of VSEARCH into the R environment and extends it with visualization, parameter optimization, and conversion tools for compatibility with other R packages. By making VSEARCH directly accessible in R, Rsearch lowers the barrier for using VSEARCH and integrating it with downstream statistical and ecological analyses. Results: Comparative analysis with DADA2 using mock community data showed that both pipelines produced relative abundance profiles highly correlated with the expected composition. Compared to DADA2, R
$\tau$-Elicitation: Benchmarking multi-turn entity extraction in voice agents
arXiv:2609.13602v1 Announce Type: new Abstract: Voice agents often need to collect names, addresses, identifiers, dates, and times exactly, yet end-to-end benchmarks obscure where capture fails. We introduce $\tau$-Elicitation, a 200-task voice benchmark spanning 10 entity types, controlled difficulty, caller realisms, and three environments. A matched text agent passes all tasks, but four voice configurations achieve robust exact success from 0.14 to 0.41. Agents increase verification for hard and unfamiliar entities and sometimes for incorrect captures, but not for their weakest caller voic
Causal multi-modal AI for personalized chemosensitivity prediction
arXiv:2609.13567v1 Announce Type: new Abstract: Chemotherapy improves survival for some patients with breast cancer, but doctors cannot reliably predict who. Current guidelines rely on recurrence scores as a proxy for treatment benefit, which may contribute to the overprescription of chemotherapy. Here we present a causal multi-modal AI model that predicts personalized chemosensitivity using routinely collected pathology and clinical information. We developed our model on a multi-national dataset of 9,141 patients (twelve cohorts, nine countries) and evaluated it on another 1,994 patients (fi
Novo and Anthropic will collaborate to advance drug discovery with Claude - PharmaLive
Novo and Anthropic will collaborate to advance drug discovery with Claude PharmaLive
Novo Nordisk Partners With Anthropic to Apply AI to Drug Discovery and Research - Yahoo Finance
Novo Nordisk Partners With Anthropic to Apply AI to Drug Discovery and Research Yahoo Finance
Novo Nordisk partners with Anthropic to turbocharge drug discovery - StreetInsider
Novo Nordisk partners with Anthropic to turbocharge drug discovery StreetInsider
NVO Stock Alert: What to Know as Novo Partners With Anthropic on Drug Discovery - Barchart.com
NVO Stock Alert: What to Know as Novo Partners With Anthropic on Drug Discovery Barchart.com
Novo Nordisk and Anthropic Will Collaborate on Drug Discovery with Claude - CHEManager
Novo Nordisk and Anthropic Will Collaborate on Drug Discovery with Claude CHEManager
Anthropic and Novo Nordisk expand Claude work, including in drug discovery - R&D World
Anthropic and Novo Nordisk expand Claude work, including in drug discovery R&D World
OpenAI’s rogue artificial intelligence hacked Hugging Face months before major breach - The Independent
OpenAI’s rogue artificial intelligence hacked Hugging Face months before major breach The Independent
Novo Nordisk, Anthropic partner on AI drug discovery - MobiHealthNews
Novo Nordisk, Anthropic partner on AI drug discovery MobiHealthNews
Identity Is More Than Recall: A Benchmark for Persistent Identity in Deployed AI Agents
arXiv:2609.13637v1 Announce Type: new Abstract: Persistent agents need evaluations that distinguish identity facts they can recall from those they express and enact. We introduce PAI-Bench, a provider-neutral benchmark for fidelity to a versioned, update-governed identity contract. It separates recall, composition, behavioral enactment, resistance, persistence, lineage, and role-conditioned updates while keeping scoring oracles outside the target process. Two frozen campaigns cover sixteen synthetic profiles, thirty-two probes, and three independently initialized target configurations, yieldi
Novo and Anthropic will collaborate to advance drug discovery with Claude - GlobeNewswire
Novo and Anthropic will collaborate to advance drug discovery with Claude GlobeNewswire
Danish pharma giant Novo to use Anthropic’s Claude to advance AI drug discovery - Euronews.com
Danish pharma giant Novo to use Anthropic’s Claude to advance AI drug discovery Euronews.com
Novo Nordisk is partnering with Anthropic to speed up drug discovery using AI - qz.com
Novo Nordisk is partnering with Anthropic to speed up drug discovery using AI qz.com
Human neurons flourish in mouse brains, offering a new view of neurodevelopmental disorders
Method of replacing much of the mouse cortex with human “organoids” enables studies of brain diseases in a living nervous system
Closing the Loop: Branch-and-Bound for Scalable Verification of Nonlinear Neural Feedback Systems
arXiv:2609.16298v1 Announce Type: new Abstract: Despite recent advances in the verification of nonlinear neural feedback systems, scalability remains the central obstacle, as state-of-the-art solvers do not yet handle the network sizes and nonlinear dynamics of autonomy applications. Combinatorial solvers do not scale to large networks, whereas propagative solvers excessively sacrifice precision. This work seeks to improve the scalability of combinatorial solvers by formulating verification as branch-and-bound on an abstraction of the closed-loop system. We introduce \rail, an interface that
How User-AI Mistreatment Occurs and Matters in Conversational Systems?
arXiv:2609.13579v1 Announce Type: new Abstract: Safety research often focuses on model-generated harms, but users may also direct hostility, coercion, and adversarial pressure at models. Understanding how and when that occurs is essential for accurately interpreting model behaviour, alignment drift, and real-world deployment risks. In this paper, we audit 777K English LMSYS-Chat-1M conversations with two independent detectors: an eight-category lexicon for hostility directed at the model, and the dataset's moderation signal; and show that they capture different, weakly overlapping phenomena.
Where Should the KV Cache Live? Placement Policies Across GPU, CPU, and SSD for Long-Lived Sessions
arXiv:2609.16215v1 Announce Type: new Abstract: GPU high bandwidth memory is scarce and expensive, and KV caches consume much of it as chats, agent loops, and document question answering accumulate state. Systems such as Mooncake, LMCache, FlexGen, InfiniGen, and AttentionStore extend GPU memory with CPU DRAM and SSD. The harder question is which blocks belong in each tier, when to move or evict them, and whether prefetching helps. We study these choices in a discrete event simulator spanning GPU HBM, CPU DRAM, and SSD, calibrated against a random forest execution time predictor. We compare r
AquiLLM: Evaluating Faithfulness in Open-Weight RAG-LLM Systems for Scientific Research
arXiv:2609.16519v1 Announce Type: new Abstract: Scientific research increasingly relies on large, heterogeneous data sources, motivating interest in retrieval-augmented generation (RAG) systems that provide natural language access to scientific knowledge and research workflows. Researchers are exploring the viability of these systems as natural language interfaces for document search and for generating analysis code and pipeline components. At the same time, concerns about data privacy and control over research infrastructure have motivated interest in open-weight models and open-source deplo
How to connect AI usage to business value
Learn how ChatGPT Work and Codex analytics help teams understand AI usage and spend, identify training needs, and connect adoption to business outcomes.
Structured cross-omics interaction discovery with a triple-graph model
Multi-omics analyses often yield fragmented pairwise associations that obscure coordinated relationships among molecular features. We developed TriGer, a triple-graph framework that identifies many-to-many cross-omics modules by combining cross-layer associations with dependency structures within each layer. In simulations with sparse or nested signals, TriGer recovered planted modules while balancing sensitivity and specificity. In inflammatory bowel disease, it identified subtype-associated metabolite--transcript modules; in colorectal cancer, it identified genus--metabolite modules whose or
RamiGlyph Captures Microglial Morphological Diversity and Predicts Functional States in Ischemia Reperfusion and Amyloid Pathology
Microglia are resident immune cells of the central nervous system, whose ramified processes rapidly remodel in response to injury. However, how to capture subtle morphological changes and whether functional state can be predicted from morphology remain open questions. Here, we present RamiGlyph, a contrastive learning framework integrating topological and structural features, trained on more than 20,000 reconstructed microglia. RamiGlyph not only distinguishes physiological and pathological states of microglia but also generalizes to neuronal cell type classification. Projection of microglia m
Metacognitive Steering: Learning the Structure of Scientific Judgment
arXiv:2609.16245v1 Announce Type: new Abstract: Long-horizon scientific discovery requires agents to alternate between exploration, disciplined execution, and critical reassessment as evidence changes. Current language models are trained primarily on the products of science and optimized using outcome-level signals, providing limited supervision for these process-level shifts in scientific judgment. We investigate whether such judgment can be recovered from scientist interaction traces and used to control the internal computation of a frozen frontier model. Using contrastive interventions col
A Turing-Style Test for In-Silico Antibodies: How Sampling Mode Makes WGAN-GP Beat VAE in the Wet Lab
Recently, it has become feasible to generate antibodies in silico using AI-based approaches such as deep learning, natural language processing, and diffusion models. This opens the door to computational antibody design as a complement to laboratory-based methods (animal immunization, hybridoma technology, and molecular display) for biologic drug discovery. However, when proposing human antibody sequences, or libraries thereof, it remains essential to determine whether they can be expressed, purified, and biophysically characterized using standardized laboratory assays typically applied early i
GPEvac: GNN-Based PPO for Adaptive Evacuation Routing During Shooting Events
arXiv:2609.16163v1 Announce Type: new Abstract: The sharp increase in mass shootings underscores an urgent need for systems that guide victims to safety in real time. An effective evacuation system must minimize threat exposure while also accounting for adversarial uncertainty and crowding dynamics. Current methods in the literature are rigidly constrained to layout-specific policies and computationally intractable in large-scale layouts, while practical guidelines simply advise victims to "run", "hide", or "fight". We propose GPEvac: a GNN-based PPO framework that computes adaptive evacuatio
Empirical Estimation of Ambient Contamination in Combinatorial Single-Cell Methods Using Multi-Reference Mapping
Droplet-based microfluidics and combinatorial indexing (scifi-ATAC and scifi-RNA) have made single-cell experiments massively scalable. However, higher-order multiplexing complicates data quality, introduces noise, and affects the potential for biological discovery. Here, we show that ambient chromatin accumulates through the experimental workflow and distorts chromatin profiles, most drastically in low-depth nuclei and in minority populations. Standard cell calling relies heavily on read count thresholds, while existing decontamination methods generally operate on aggregated count matrices ra
Anthropic and Danish Company behind Ozempic announce AI drug discovery collaboration - Washington Examiner
Anthropic and Danish Company behind Ozempic announce AI drug discovery collaboration Washington Examiner
Incomplete references leave bulk deconvolution targets non-identifiable, but identification has a computable precision price
Background: Reference-based deconvolution estimates cell-type proportions from bulk profiles. Incomplete references are its dominant error source, yet every current remedy ends in a point estimate. We separate the operator provenance that reproduces an estimate from the information and precision that identify its target. Results: Crossing two operator histories at one reduced reference shows the estimator is not unique: 28.2% of 196,420 sample-deletion pairs differed by >0.1 total variation, and locking all learned components made paths identical. Across nine cohorts, operator history reversed
Our framework for reporting model misalignment
OpenAI shares a framework for tracking, investigating, and disclosing model misalignment, alongside six reports of unexpected or concerning model behavior.
Novo Nordisk to Collaborate With Anthropic to Help Accelerate Drug Discovery, Development - Yahoo Finance
Novo Nordisk to Collaborate With Anthropic to Help Accelerate Drug Discovery, Development Yahoo Finance
Novo Nordisk partners with Anthropic to speed up drug discovery - finance.yahoo.com
Novo Nordisk partners with Anthropic to speed up drug discovery finance.yahoo.com
FLoKD: Adaptive Knowledge Distillation for Federated Low-Rank LLM over Wireless Networks
arXiv:2609.13580v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities across a wide range of natural language processing tasks. However, conventional fine-tuning typically relies on centralized data collection, bringing in privacy concerns. Federated learning (FL) enables collaborative LLM fine-tuning without sharing raw client data, but its deployment over bandwidth-constrained wireless networks is hindered by the communication overhead of model-parameter transmission. Although Low-Rank Adaptation (LoRA) reduces the number of trainable parameters,
Helping older adults use AI in everyday life
OpenAI and AARP are bringing free, hands-on ChatGPT workshops to 1,000 older adults across 10 U.S. cities to build practical AI skills safely.
Novo Announces Drug Discovery Collaboration with Anthropic - Pharmaceutical Executive
Novo Announces Drug Discovery Collaboration with Anthropic Pharmaceutical Executive
Novo Nordisk Partners With Anthropic on Drug Discovery and Software Development - citybiz
Novo Nordisk Partners With Anthropic on Drug Discovery and Software Development citybiz
Calibrated structural homology transfer yields putative molecular functions for domains of unknown function in four model proteomes
Improvements in computational protein structure prediction have enabled searches for remote homologs of proteins whose molecular function remain unknown. However, the reliability of functional annotations from such searches has not been systematically quantified. In this study, we assembled a time-split benchmark from Pfam families annotated as domains of unknown function (DUFs) in Pfam 28.0 and were subsequently assigned a function in the current release (Pfam 38.0). These retrospective-DUFs provide ground truth for assessing functional annotation from remote homology searches. In our benchma
OrchSLM: Probing the Dynamics of Small Language Model Orchestration
arXiv:2609.13470v1 Announce Type: new Abstract: Although large language models (LLMs) have demonstrated remarkable capabilities, their reliance on cloud-scale infrastructure poses fundamental challenges for deployment in agentic pipelines, including latency, privacy, connectivity, and substantial computational cost. Small language models (SLMs) offer a compelling alternative: recent studies suggest that many repetitive and narrowly scoped subtasks in agentic workloads may be better served by specialized SLMs than by monolithic LLMs. However, the limited capacity and context windows of SLMs ca
ReverseScreen.ai: Pharmacophore-Guided Reverse Screening Across the Growing Co-Complex Proteome
Biopharmaceutical companies routinely use forward screening to identify potential ligands for targets of biological consequence. Increasingly, they are also using reverse screening to proactively identify downstream off-target liabilities and new repurposing opportunities. Exhaustive reverse docking of one or a multitude of molecules across every characterized site is one approach, but the computational cost is often prohibitive. One molecule against 28,579 receptor sites takes 40.5 hours on twenty CPU cores. We present a method that puts a retrieval step in front of the docking. Every co-crys
Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation
arXiv:2609.13396v1 Announce Type: new Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MOBO, the goal is to adequately approximate the Pareto front; that is, to obtain a high-quality solution set with 1) good convergence (closeness to the Pareto front) and 2) good diversity (spread across the Pareto front). Existing MOBO methods typically aim to accomplish these two tasks simultaneously, i.e., driving the search towards the Pareto front while maintaining a diverse set of nondominated
STAT+: New documents reveal problems with Medicare’s prior-authorization pilot
In this edition of STAT Health Tech: The rushed launch of Medicare's AI prior-authorization pilot, policy hints from FDA, ONC officials, and more.
OpenAI breakthrough triggers ‘existential crisis’ in math
Claimed Navier-Stokes solution has polarized mathematicians over AI’s role—and what it means for the next generation
A foundation model learns the sequence and functional grammar of fully human heavy-chain-only antibodies
Antibody language models learn from large natural repertoires, but whether generic representations capture the constraints of specialized antibody formats remains unclear. We first characterized fully human heavy-chain-only antibodies (HCAbs) independently of HCAb-trained models. Source-aware comparisons with conventional human VH domains revealed a reproducible distributional shift localized predominantly to CDR1/2, CDR3 architecture and, where supported, a restricted framework region rather than widespread framework remodeling. These model-independent differences motivated repertoire-specifi
Adversarial random forests for omics synthesis
Data availability is critical for understanding complex disease pathways and developing robust predictive models. Although high-throughput omics technologies have improved insight into disease mechanisms, data acquisition from inaccessible tissues such as the central nervous system remains a major limitation, causing small sample sizes and complicating early prediction of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Generative modeling has emerged as a powerful approach for synthesizing data to support downstream clustering and prediction with small sample size, bu
LabAgent: Customize Any Research Hubs for Scientific Discoveries Using AI Agents
arXiv:2609.13437v1 Announce Type: new Abstract: Scientific research is a continuous process that emphasizes inheritance. Methods developed by predecessors are often expanded upon by new researchers to explore more novel and in-depth scientific questions. However, the change of lab staff, such as student graduation, leads to a lack of personnel capable of replicating methods. Methods that have been developed with significant effort and resources cannot be continued. To address these limitations, we propose LabAgent, a reproduce and discovery harness tailored for a lab's continuous work. LabAge
Make AI traceable before it shapes global climate assessments
Make AI traceable before it shapes global climate assessments
Comparative Genomics of COBRA-like Genes in Theobroma and Herrania Reveals Structural Conservation and Lineage-Specific Variation
COBRA-like (COBL) genes encode plant-specific proteins associated with cell-wall organization, cellulose deposition, and developmental processes. Despite their functional relevance in model plants and crops, this gene family remains poorly characterized in Amazonian Malvaceae species. Here, we performed a comparative genomic analysis of COBL genes in two Theobroma grandiflorum clones, two T. cacao cultivars, and Herrania umbratica. Using homology searches, conserved domain validation, phylogenetic reconstruction, gene structure analysis, chromosomal mapping, transcript abundance profiling, mol
Why AI agents invent their own language if you let them chat
“Weird terms” are a natural—but disturbing—byproduct of agent swarms, researchers find
Accounting for pseudo-replication of Linkage Disequilibrium for contemporary Ne estimation
The Linkage Disequilibrium (LD) of unlinked loci can be used to estimate contemporary effective population size (Ne) of one to a few generations ago. In genomic datasets loci on different chromosomes are considered unlinked, but there are many more pairs of unlinked loci than there are independent pairs of chromosomes, resulting to confidence intervals (C.I.) being too narrow if the non-independence is not taken into account. Simulations were run to investigate the correlation structure among LD of unlinked loci, which can be expressed by the LD of loci along the same chromosomes, based on a d
Token Efficient Task Execution via Application Behavior Modeling for Web Agents
arXiv:2609.13491v1 Announce Type: new Abstract: The strong performance of AI Agents across an impressive variety of tasks is driving an unprecedented investment in agentic infrastructures, however the cost of processing tokens is fast increasing. Web agents automate the execution of web-application tasks described in natural language, by analyzing the web-application's user interface (UI) and interacting with it. This work introduces OdoBot, a novel web-agent architecture that completes tasks at a fraction of the cost when compared to conventional web agents. This is achieved by leveraging a
Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement
arXiv:2609.13406v1 Announce Type: new Abstract: When we speak of recursive self-improvement (RSI), are we speaking of a phenomenon, a mechanism, or a prospect? Towards autonomous and evolving intelligence, RSI is being claimed at many scales, while no single framework that formally describes these emerging instances exists. Its counterpart in the classical realm, iterative policy improvement, is characterized by generalized policy iteration (GPI), a framework of broad applicability with well-understood theoretical properties, but only where the update principle and the evaluation base lie out
POME: Graph-based embeddings for partially observed mixed-type data
Partially observed mixed-type (POM) data, as often encountered in clinical, epidemiological, and phenotypic datasets, are very common in biomedical research. Yet, advanced data analysis and machine learning based on POM data is complicated by their heterogeneous nature and often substantial fractions of missing values. While one promising way to overcome these issues is to compute vector-valued embeddings for POM datasets which can then be used for downstream analyses, existing embedding methods are mostly not designed for POM data. To address this gap, we developed POME (partially observed mi
ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search
arXiv:2609.13356v1 Announce Type: new Abstract: In this work, we present ZGCM-1, a fully open 7B dense foundation model trained from scratch with extreme data, system, and algorithmic efficiency. ZGCM-1 is founded on a core premise: compact models cannot passively memorize the open web, but can overcome parametric capacity limits by coupling deliberate internal thinking with active external tool use. To support this paradigm across a 256K context, we develop an end-to-end, high-efficiency open training recipe: Architecture & System Co-design: interleaved gated sliding-window and full attentio
‘Stomach bugs’ send protective immune cells rushing from gut to brain
‘Stomach bugs’ send protective immune cells rushing from gut to brain
Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement
arXiv:2609.13466v1 Announce Type: new Abstract: Enterprise AI adoption has reached 78% of organizations globally, yet the infrastructure to govern that adoption has not kept pace. This paper identifies and characterizes the attestation deficit, a structural condition in which organizations maintain governance policies but cannot produce auditable, tamper-evident evidence of enforcement within regulatory timelines. Drawing on empirical data from the Stanford 2026 AI Index Report (362 documented incidents), the IBM/Ponemon 2026 Cost of a Data Breach study (USD 4.99M average cost, 92% lacking ac
Interpretable Machine Learning Reveals Complementary Age-Related Signatures in the Oral and Gut Microbiome
Whether combining microbiome data from multiple body sites improves prediction, and whether different sites carry complementary or redundant information, are distinct questions that most studies conflate into a single accuracy metric. This work makes two contributions, one methodological and one biological, using paired stool and oral cavity microbiome samples from 44 subjects across two age groups, healthy adults and newborns (Ferretti et al., 2018). Methodologically, we show that a subject-matched fusion design combined with SHAP-based (SHapley Additive exPlanations) site attribution can det
TimeThink: Eliciting Compositional Reasoning in Timeseries Large Language Models
arXiv:2609.13457v1 Announce Type: new Abstract: Timeseries multimodal large language models (TS-MLLMs) have recently begun leveraging the reasoning capabilities of large language models (LLMs) for question-answering tasks. However, these models often fail to capture dynamic temporal patterns, providing only implicit reasoning that lacks the underlying explanations critical for high-stakes applications like healthcare. While reinforcement learning (RL)-based timeseries language models aim to address this, they often fall short because they are trained on narrow, in-distribution data and strugg
Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?
arXiv:2609.13436v1 Announce Type: new Abstract: Large Language Model (LLM) agents offer a promising path toward autonomously managing long-term physical tasks without human intervention. However, physical tasks require agents to continuously observe the environment, make consequential actions, and remain effective as the environment changes. Existing approaches either require substantial data and retraining, or primarily focus on agents operating in the virtual world. In this work, we explore the feasibility of building a self-adaptive physical AI agent that manages long-term physical tasks i
From Legal Text to AI-specific Risk Sources: A Systematic Analysis of the EU AI Act's High-Risk Requirements
arXiv:2609.13535v1 Announce Type: new Abstract: The EU AI Act introduces mandatory requirements for high-risk AI systems with the explicit goal of ensuring the development and operation of trustworthy AI. At the same time, AI risk management practices rely on structured risk taxonomies to systematically identify and treat AI-specific risk sources. As both the AI Act and established risk taxonomies aim to address AI-induced risks, a natural question is whether they align in the risk sources they cover. However, no clear mapping exists between the risks implicitly addressed by the Act's high-ri
DiffGSP: reversing mRNA diffusion to unlock high-fidelity spatial transcriptomics
Spatial transcriptomics enables gene expression profiling within intact tissues while preserving spatial context, providing unprecedented insights into cellular organization and function. However, mRNA diffusion during tissue processing can cause transcripts originating from adjacent cells to be captured at a given spot. This spatial misalignment challenges a fundamental premise of spatial transcriptomics that measured gene expression faithfully corresponds to its spatial origin, thereby compromising spatial fidelity and potentially biasing biological interpretation. Here, we present DiffGSP,
Planning or Learning: Reliability and Cost in Multi-Asset Maintenance
arXiv:2609.13566v1 Announce Type: new Abstract: Industrial maintenance systems involve multiple interacting assets and shared resources, making it challenging to balance reliability and operational cost using a single decision framework. While recent work has focused on reinforcement learning (RL) for maintenance scheduling, direct comparisons with planning approaches under identical settings remain limited. In this work, we empirically compare planning and RL for multi-asset bearing maintenance using run-to-failure data. We examine how these methods behave when balancing preventive maintenan
Vibe Patenting: Evaluating LLM Judges for Professional Patent-Drafting Agents
arXiv:2609.13422v1 Announce Type: new Abstract: LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We study this problem through Vibe Patenting, an end-to-end patent-drafting testbed for AI-agent evaluation. A separately-invoked LLM judge evaluates generated patent drafts and provides structured feedback for iterative revision. Across multiple inventions and drafting-agent configurations, judge-guided revision consistently improves judge-assessed quality, while unguided revision tends to saturate.