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Received — 4 December 2025 ⏭ Favourites

A Definition of AGI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dan Hendrycks · Dawn Song · Christian Szegedy · Honglak Lee · Yarin Gal · Erik Brynjolfsson · Sharon Li · Andy Zou · Lionel Levine · Bo Han · Jie Fu · Ziwei Liu · Jinwoo Shin · Kimin Lee · Mantas Mazeika · Long Phan · George Ingebretsen · Adam Khoja · Cihang Xie · Olawale Salaudeen · Matthias Hein · Kevin Zhao · Alexander Pan · David Duvenaud · Bo Li · Steve Omohundro · Gabriel Alfour · Max Tegmark · Kevin McGrew · Gary Marcus · Jaan Tallinn · Eric Schmidt · Yoshua Bengio
4 December 2025 at 13:00
arXiv:2510.18212v3 Announce Type: replace Abstract: The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly "jagged" cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI.
Received — 3 December 2025 ⏭ Favourites

Rethinking Lung Cancer Screening: AI Nodule Detection and Diagnosis Outperforms Radiologists, Leading Models, and Standards Beyond Size and Growth

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sylvain Bodard · Pierre Baudot · Benjamin Renoust · Charles Voyton · Gwendoline De Bie · Ezequiel Geremia · Van-Khoa Le · Danny Francis · Pierre-Henri Siot · Yousra Haddou · Vincent Bobin · Jean-Christophe Brisset · Carey C. Thomson · Valerie Bourdes · Benoit Huet
2 December 2025 at 13:00
arXiv:2512.00281v1 Announce Type: cross Abstract: Early detection of malignant lung nodules is critical, but its dependence on size and growth in screening inherently delays diagnosis. We present an AI system that redefines lung cancer screening by performing both detection and malignancy diagnosis directly at the nodule level on low-dose CT scans. To address limitations in dataset scale and explainability, we designed an ensemble of shallow deep learning and feature-based specialized models. Trained and evaluated on 25,709 scans with 69,449 annotated nodules, the system outperforms radiologists, Lung-RADS, and leading AI models (Sybil, Brock, Google, Kaggle). It achieves an area under the receiver operating characteristic curve (AUC) of 0.98 internally and 0.945 on an independent cohort. With 0.5 false positives per scan at 99.3\% sensitivity, it addresses key barriers to AI adoption. Critically, it outperforms radiologists across all nodule sizes and stages, excelling in stage 1 cancers, and all growth-based metrics, including the least accurate: Volume-Doubling Time. It also surpasses radiologists by up to one year in diagnosing indeterminate and slow-growing nodules.

Human Decision-making is Susceptible to AI-driven Manipulation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sahand Sabour · June M. Liu · Siyang Liu · Chris Z. Yao · Shiyao Cui · Xuanming Zhang · Wen Zhang · Yaru Cao · Advait Bhat · Jian Guan · Wei Wu · Rada Mihalcea · Hongning Wang · Tim Althoff · Tatia M. C. Lee · Minlie Huang
2 December 2025 at 13:00
arXiv:2502.07663v3 Announce Type: replace Abstract: AI systems are increasingly intertwined with daily life, assisting users with various tasks and guiding decision-making. This integration introduces risks of AI-driven manipulation, where such systems may exploit users' cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes. Through a randomized between-subjects experiment with 233 participants, we examined human susceptibility to such manipulation in financial (e.g., purchases) and emotional (e.g., conflict resolution) decision-making contexts. Participants interacted with one of three AI agents: a neutral agent (NA) optimizing for user benefit without explicit influence, a manipulative agent (MA) designed to covertly influence beliefs and behaviors, or a strategy-enhanced manipulative agent (SEMA) equipped with established psychological tactics, allowing it to select and apply them adaptively during interactions to reach its hidden objectives. By analyzing participants' preference ratings, we found significant susceptibility to AI-driven manipulation. Particularly across both decision-making domains, interacting with the manipulative agents significantly increased the odds of rating hidden incentives higher than optimal options (Financial, MA: OR=5.24, SEMA: OR=7.96; Emotional, MA: OR=5.52, SEMA: OR=5.71) compared to the NA group. Notably, we found no clear evidence that employing psychological strategies (SEMA) was overall more effective than simple manipulative objectives (MA) on our primary outcomes. Hence, AI-driven manipulation could become widespread even without requiring sophisticated tactics and expertise. While our findings are preliminary and derived from hypothetical, low-stakes scenarios, we highlight a critical vulnerability in human-AI interactions, emphasizing the need for ethical safeguards and regulatory frameworks to protect human autonomy.
Received — 21 November 2025 ⏭ Favourites

Latent plasticity of the human pancreas across development, health, and disease

✇(Multiomics OR Omics) AND (Pancreatic)
By: Elisabetta Mereu · Diego Balboa · Johannes Liebig · Aitor Gonzalez-Herrero · Anna Martinez-Casals · Mariya Mardamshina · Fanny Mollandin · Felix Schicktanz · Luca Tosti · Valerie Vandenbempt · Dana Avrahami · Edgar Bernardo · Frida Björklund · Robert Lorenz Chua · Marten Engelse · Javier García-Hurtado · Nathalie Groen · Maaike Hanegraaf · Pablo Iañez · Katharina Jechow · Björn Konukiewitz · Christian Lawerenz · Domenica Marchese · Mauro J Muraro · Silvia Pellegrini · Valeria Sordi · Alexander Sudy · Ulrike Taron · Foo Wei Ten · Timo Trefzer · Sven Twardziok · Maarten van Agen · Françoise Carlotti · Eelco de Koning · Jorge Ferrer · Benjamin Glaser · Holger Heyn · Emma Lundberg · Lorenzo Piemonti · Katja Steiger · Alexander van Oudenaarden · Wilko Weichert · Christian Conrad · Roland Eils
19 November 2025 at 19:00

bioRxiv [Preprint]. 2025 Oct 3:2025.10.01.679230. doi: 10.1101/2025.10.01.679230.

ABSTRACT

The pancreas plays a central role in major human diseases, yet our understanding of its cellular diversity and plasticity remains incomplete. Here, we present a single-cell multiomics atlas of the human pancreas, profiling over four million cells and nuclei from 57 donors across fetal development, adult homeostasis, and type 2 diabetes (T2D). Integrating sc/snRNA-seq, snATAC-seq, VASA-seq, spatial transcriptomics (Xenium), and multiplexed proteomics (CODEX), we resolve gene expression, chromatin accessibility, and spatial organization at high resolution. We identify transcriptionally plastic centroacinar-like cells (pCACs) in adults with fetal-like features, delineate endocrine and exocrine lineage trajectories during development, and uncover HNF1A-defined beta cell epigenetic states. In T2D, we observe shifts in beta cell subtypes and altered regulatory programs. Glucose perturbation of healthy islets reveals cell-type-specific adaptation and stress responses. This atlas provides a foundational framework to understand pancreas biology and the role of cellular plasticity in regeneration and disease.

PMID:41256699 | PMC:PMC12622017 | DOI:10.1101/2025.10.01.679230

Received — 18 November 2025 ⏭ Favourites

CLINB: A Climate Intelligence Benchmark for Foundational Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Michelle Chen Huebscher · Katharine Mach · Aleksandar Stani\'c · Markus Leippold · Ben Gaiarin · Zeke Hausfather · Elisa Rawat · Erich Fischer · Massimiliano Ciaramita · Joeri Rogelj · Christian Buck · Lierni Sestorain Saralegui · Reto Knutti
18 November 2025 at 13:00
arXiv:2511.11597v1 Announce Type: new Abstract: Evaluating how Large Language Models (LLMs) handle complex, specialized knowledge remains a critical challenge. We address this through the lens of climate change by introducing CLINB, a benchmark that assesses models on open-ended, grounded, multimodal question answering tasks with clear requirements for knowledge quality and evidential support. CLINB relies on a dataset of real users' questions and evaluation rubrics curated by leading climate scientists. We implement and validate a model-based evaluation process and evaluate several frontier models. Our findings reveal a critical dichotomy. Frontier models demonstrate remarkable knowledge synthesis capabilities, often exhibiting PhD-level understanding and presentation quality. They outperform "hybrid" answers curated by domain experts assisted by weaker models. However, this performance is countered by failures in grounding. The quality of evidence varies, with substantial hallucination rates for references and images. We argue that bridging this gap between knowledge synthesis and verifiable attribution is essential for the deployment of AI in scientific workflows and that reliable, interpretable benchmarks like CLINB are needed to progress towards building trustworthy AI systems.

End to End AI System for Surgical Gesture Sequence Recognition and Clinical Outcome Prediction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xi Li · Nicholas Matsumoto · Ujjwal Pasupulety · Atharva Deo · Cherine Yang · Jay Moran · Miguel E. Hernandez · Peter Wager · Jasmine Lin · Jeanine Kim · Alvin C. Goh · Christian Wagner · Geoffrey A. Sonn · Andrew J. Hung
18 November 2025 at 13:00
arXiv:2511.11899v1 Announce Type: new Abstract: Fine-grained analysis of intraoperative behavior and its impact on patient outcomes remain a longstanding challenge. We present Frame-to-Outcome (F2O), an end-to-end system that translates tissue dissection videos into gesture sequences and uncovers patterns associated with postoperative outcomes. Leveraging transformer-based spatial and temporal modeling and frame-wise classification, F2O robustly detects consecutive short (~2 seconds) gestures in the nerve-sparing step of robot-assisted radical prostatectomy (AUC: 0.80 frame-level; 0.81 video-level). F2O-derived features (gesture frequency, duration, and transitions) predicted postoperative outcomes with accuracy comparable to human annotations (0.79 vs. 0.75; overlapping 95% CI). Across 25 shared features, effect size directions were concordant with small differences (~ 0.07), and strong correlation (r = 0.96, p

Speculative Decoding in Decentralized LLM Inference: Turning Communication Latency into Computation Throughput

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jingwei Song · Wanyi Chen · Xinyuan Song · Max · Chris Tong · Gufeng Chen · Tianyi Zhao · Eric Yang · Bill Shi · Lynn Ai
18 November 2025 at 13:00
arXiv:2511.11733v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model (LLM) inference by using a lightweight draft model to propose tokens that are later verified by a stronger target model. While effective in centralized systems, its behavior in decentralized settings, where network latency often dominates compute, remains under-characterized. We present Decentralized Speculative Decoding (DSD), a plug-and-play framework for decentralized inference that turns communication delay into useful computation by verifying multiple candidate tokens in parallel across distributed nodes. We further introduce an adaptive speculative verification strategy that adjusts acceptance thresholds by token-level semantic importance, delivering an additional 15% to 20% end-to-end speedup without retraining. In theory, DSD reduces cross-node communication cost by approximately (N-1)t1(k-1)/k, where t1 is per-link latency and k is the average number of tokens accepted per round. In practice, DSD achieves up to 2.56x speedup on HumanEval and 2.59x on GSM8K, surpassing the Eagle3 baseline while preserving accuracy. These results show that adapting speculative decoding for decentralized execution provides a system-level optimization that converts network stalls into throughput, enabling faster distributed LLM inference with no model retraining or architectural changes.
Received — 11 November 2025 ⏭ Favourites
Received — 10 November 2025 ⏭ Favourites
Received — 6 November 2025 ⏭ Favourites

A multimodal whole-slide foundation model for pathology

✇Nature Medicine
By: Tong Ding · Sophia J. Wagner · Andrew H. Song · Richard J. Chen · Ming Y. Lu · Andrew Zhang · Anurag J. Vaidya · Guillaume Jaume · Muhammad Shaban · Ahrong Kim · Drew F. K. Williamson · Harry Robertson · Bowen Chen · Cristina Almagro-Pérez · Paul Doucet · Sharifa Sahai · Chengkuan Chen · Christina S. Chen · Daisuke Komura · Akihiro Kawabe · Mieko Ochi · Shinya Sato · Tomoyuki Yokose · Yohei Miyagi · Shumpei Ishikawa · Georg Gerber · Tingying Peng · Long Phi Le · Faisal Mahmood
5 November 2025 at 08:00

Nature Medicine, Published online: 05 November 2025; doi:10.1038/s41591-025-03982-3

Pretrained using 335,645 whole-slide images, a foundation model is developed to provide representations for slide- and patient-level tasks. It is capable of performing clinical tasks and generating reports even in data-scarce scenarios, such as rare cancer diagnosis and survival prediction, without requiring further fine-tuning.
Received — 4 November 2025 ⏭ Favourites

Digital Twin based Automatic Reconfiguration of Robotic Systems in Smart Environments

✇cs.AI, q-bio.NC updates on arXiv.org
By: Angelos Alexopoulos · Agorakis Bompotas · Nikitas Rigas Kalogeropoulos · Panagiotis Kechagias · Athanasios P. Kalogeras · Christos Alexakos
4 November 2025 at 13:00
arXiv:2511.00094v1 Announce Type: cross Abstract: Robotic systems have become integral to smart environments, enabling applications ranging from urban surveillance and automated agriculture to industrial automation. However, their effective operation in dynamic settings - such as smart cities and precision farming - is challenged by continuously evolving topographies and environmental conditions. Traditional control systems often struggle to adapt quickly, leading to inefficiencies or operational failures. To address this limitation, we propose a novel framework for autonomous and dynamic reconfiguration of robotic controllers using Digital Twin technology. Our approach leverages a virtual replica of the robot's operational environment to simulate and optimize movement trajectories in response to real-world changes. By recalculating paths and control parameters in the Digital Twin and deploying the updated code to the physical robot, our method ensures rapid and reliable adaptation without manual intervention. This work advances the integration of Digital Twins in robotics, offering a scalable solution for enhancing autonomy in smart, dynamic environments.
Received — 31 October 2025 ⏭ Favourites

The Quest for Reliable Metrics of Responsible AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Theresia Veronika Rampisela · Maria Maistro · Tuukka Ruotsalo · Christina Lioma
31 October 2025 at 12:00
arXiv:2510.26007v1 Announce Type: cross Abstract: The development of Artificial Intelligence (AI), including AI in Science (AIS), should be done following the principles of responsible AI. Progress in responsible AI is often quantified through evaluation metrics, yet there has been less work on assessing the robustness and reliability of the metrics themselves. We reflect on prior work that examines the robustness of fairness metrics for recommender systems as a type of AI application and summarise their key takeaways into a set of non-exhaustive guidelines for developing reliable metrics of responsible AI. Our guidelines apply to a broad spectrum of AI applications, including AIS.
Received — 30 October 2025 ⏭ Favourites

Multi-omic profiling reveals age-related immune dynamics in healthy adults

✇Nature - Issue - nature.com science feeds
By: Qiuyu Gong · Mehul Sharma · Marla C. Glass · Emma L. Kuan · Aishwarya Chander · Mansi Singh · Lucas T. Graybuck · Zachary J. Thomson · Christian M. LaFrance · Samir Rachid Zaim · Tao Peng · Lauren Y. Okada · Palak C. Genge · Katherine E. Henderson · Elisabeth M. Dornisch · Erik D. Layton · Peter J. Wittig · Alexander T. Heubeck · Nelson M. Mukuka · Julian Reading · Garrett Strawn · Teminijesu Titus-Adewunmi · Kathleen Abadie · Charles R. Roll · Veronica Hernandez · Vaishnavi Parthasarathy · Tyanna J. Stuckey · Blessing Musgrove · Elliott Swanson · Cara Lord · Morgan D. A. Weiss · Cole G. Phalen · Regina R. Mettey · Kevin J. Lee · John B. Johanneson · Erin K. Kawelo · Jessica Garber · Upaasana Krishnan · Megan Smithmyer · E. John Wherry · Laura A. Vella · Sarah E. Henrickson · Mackenzie S. Kopp · Adam K. Savage · Lynne A. Becker · Paul Meijer · Ernest M. Coffey · Jorg J. Goronzy · Mikael Sigvardsson · Cate Speake · Thomas F. Bumol · Ananda W. Goldrath · Troy R. Torgerson · Xiao-jun Li · Peter J. Skene · Jane H. Buckner · Claire E. Gustafson
29 October 2025 at 08:00

Nature, Published online: 29 October 2025; doi:10.1038/s41586-025-09686-5

This multi-omic longitudinal analysis of the healthy human peripheral immune system constructs the Human Immune Health Atlas and assembles data on immune cell composition and state changes with age, including responses to cytomegalovirus infection and influenza vaccination.
Received — 29 October 2025 ⏭ Favourites

Dynamic Monitoring of Recurrent Ovarian Cancer Using Serial ctDNA: A Real-World Case Series

✇MRD
By: Eric Rios-Doria · Jonathan B Reichel · Marc R Radke · Enna Manhardt · Mayumi Rubin-Saika · Christina Lockwood · Elizabeth M Swisher · Kalyan Banda
28 October 2025 at 18:00

Curr Oncol. 2025 Oct 21;32(10):585. doi: 10.3390/curroncol32100585.

ABSTRACT

Recurrent ovarian cancer (OC) is challenging to detect early using current methods like CA-125 and imaging. Circulating tumor DNA (ctDNA) may improve disease monitoring. Here, we assess the real-world clinical utility of serial ctDNA analyses in patients with recurrent OC. We analyzed serial plasma samples (N = 23) from six patients with recurrent OC using a tumor-informed next-generation sequencing assay targeting 68 cancer-related genes developed at the University of Washington. ctDNA variant allele frequencies (VAFs) were correlated with CA-125 levels, radiographic findings, and clinical outcomes. ctDNA levels generally reflected clinical status, accurately mirroring disease progression and therapeutic response. In one patient, rising ctDNA preceded clinical recurrence by four months, despite normal CA-125 and imaging, highlighting its potential advantage. Conversely, some patients exhibited clinical progression with undetectable ctDNA, indicating limitations in assay sensitivity, biological factors, or metastatic sites (e.g., brain metastases). ctDNA and CA-125 showed complementary value in most cases, suggesting potential combined use in clinical monitoring. Our findings demonstrate that ctDNA is a promising biomarker to complement existing monitoring approaches for recurrent OC. In some cases, capable of predicting relapse and treatment response ahead of current clinical indicators. However, identified discordances underscore technical and biological challenges that warrant further investigation. Larger prospective studies are necessary to refine ctDNA's clinical utility and integration into personalized OC care.

PMID:41149505 | PMC:PMC12563156 | DOI:10.3390/curroncol32100585

Received — 28 October 2025 ⏭ Favourites

Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Study

✇cs.AI, q-bio.NC updates on arXiv.org
By: Joachim Baumann · Aleksandra Urman · Ulrich Leicht-Deobald · Zachary J. Roman · Anik\'o Hann\'ak · Markus Christen
28 October 2025 at 12:00
arXiv:2510.23578v1 Announce Type: new Abstract: The rapid adoption of generative artificial intelligence (GenAI) technologies has led many organizations to integrate AI into their products and services, often without considering user preferences. Yet, public attitudes toward AI use, especially in impactful decision-making scenarios, are underexplored. Using a large-scale two-wave survey study (n_wave1=1514, n_wave2=1488) representative of the Swiss population, we examine shifts in public attitudes toward AI before and after the launch of ChatGPT. We find that the GenAI boom is significantly associated with reduced public acceptance of AI (see Figure 1) and increased demand for human oversight in various decision-making contexts. The proportion of respondents finding AI "not acceptable at all" increased from 23% to 30%, while support for human-only decision-making rose from 18% to 26%. These shifts have amplified existing social inequalities in terms of widened educational, linguistic, and gender gaps post-boom. Our findings challenge industry assumptions about public readiness for AI deployment and highlight the critical importance of aligning technological development with evolving public preferences.

ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mohammed Baharoon · Luyang Luo · Michael Moritz · Abhinav Kumar · Sung Eun Kim · Xiaoman Zhang · Miao Zhu · Mahmoud Hussain Alabbad · Maha Sbayel Alhazmi · Neel P. Mistry · Lucas Bijnens · Kent Ryan Kleinschmidt · Brady Chrisler · Sathvik Suryadevara · Sri Sai Dinesh Jaliparthi · Noah Michael Prudlo · Mark David Marino · Jeremy Palacio · Rithvik Akula · Di Zhou · Hong-Yu Zhou · Ibrahim Ethem Hamamci · Scott J. Adams · Hassan Rayhan AlOmaish · Pranav Rajpurkar
28 October 2025 at 12:00
arXiv:2507.22030v2 Announce Type: replace-cross Abstract: We introduce ReXGroundingCT, the first publicly available dataset linking free-text findings to pixel-level 3D segmentations in chest CT scans. The dataset includes 3,142 non-contrast chest CT scans paired with standardized radiology reports from CT-RATE. Construction followed a structured three-stage pipeline. First, GPT-4 was used to extract and standardize findings, descriptors, and metadata from reports originally written in Turkish and machine-translated into English. Second, GPT-4o-mini categorized each finding into a hierarchical ontology of lung and pleural abnormalities. Third, 3D annotations were produced for all CT volumes: the training set was quality-assured by board-certified radiologists, and the validation and test sets were fully annotated by board-certified radiologists. Additionally, a complementary chain-of-thought dataset was created to provide step-by-step hierarchical anatomical reasoning for localizing findings within the CT volume, using GPT-4o and localization coordinates derived from organ segmentation models. ReXGroundingCT contains 16,301 annotated entities across 8,028 text-to-3D-segmentation pairs, covering diverse radiological patterns from 3,142 non-contrast CT scans. About 79% of findings are focal abnormalities and 21% are non-focal. The dataset includes a public validation set of 50 cases and a private test set of 100 cases, both annotated by board-certified radiologists. The dataset establishes a foundation for enabling free-text finding segmentation and grounded radiology report generation in CT imaging. Model performance on the private test set is hosted on a public leaderboard at https://rexrank.ai/ReXGroundingCT. The dataset is available at https://huggingface.co/datasets/rajpurkarlab/ReXGroundingCT.

Benchmarking large language models for personalized, biomarker-based health intervention recommendations

✇npj Digital Medicine
By: Hans Jarchow · Christoph Bobrowski · Steffi Falk · Andreas Hermann · Anton Kulaga · Johann-Christian Põder · Maximilian Unfried · Nikolay Usanov · Bijan Zendeh · Brian K. Kennedy · Sebastian Lobentanzer · Georg Fuellen
27 October 2025 at 08:00

npj Digital Medicine, Published online: 27 October 2025; doi:10.1038/s41746-025-01996-2

Benchmarking large language models for personalized, biomarker-based health intervention recommendations

A Definition of AGI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dan Hendrycks · Dawn Song · Christian Szegedy · Honglak Lee · Yarin Gal · Erik Brynjolfsson · Sharon Li · Andy Zou · Lionel Levine · Bo Han · Jie Fu · Ziwei Liu · Jinwoo Shin · Kimin Lee · Mantas Mazeika · Long Phan · George Ingebretsen · Adam Khoja · Cihang Xie · Olawale Salaudeen · Matthias Hein · Kevin Zhao · Alexander Pan · David Duvenaud · Bo Li · Steve Omohundro · Gabriel Alfour · Max Tegmark · Kevin McGrew · Gary Marcus · Jaan Tallinn · Eric Schmidt · Yoshua Bengio
27 October 2025 at 12:00
arXiv:2510.18212v2 Announce Type: replace Abstract: The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly "jagged" cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI.
Received — 24 October 2025 ⏭ Favourites

VaultGemma: A Differentially Private Gemma Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Amer Sinha · Thomas Mesnard · Ryan McKenna · Daogao Liu · Christopher A. Choquette-Choo · Yangsibo Huang · Da Yu · George Kaissis · Zachary Charles · Ruibo Liu · Lynn Chua · Pritish Kamath · Pasin Manurangsi · Steve He · Chiyuan Zhang · Badih Ghazi · Borja De Balle Pigem · Prem Eruvbetine · Tris Warkentin · Armand Joulin · Ravi Kumar
24 October 2025 at 12:00
arXiv:2510.15001v2 Announce Type: replace-cross Abstract: We introduce VaultGemma 1B, a 1 billion parameter model within the Gemma family, fully trained with differential privacy. Pretrained on the identical data mixture used for the Gemma 2 series, VaultGemma 1B represents a significant step forward in privacy-preserving large language models. We openly release this model to the community
Received — 21 October 2025 ⏭ Favourites

Parity and lactation induce T cell mediated breast cancer protection

✇Nature - Issue - nature.com science feeds
By: Balaji Virassamy · Franco Caramia · Peter Savas · Michael A. Harris · Jia-Wern Pan · Jianan Wang · Emmaline Brown · Megan M. R. O’Malley · Courtney T. van Geelen · Michael Hun · Thomas N. Burn · Sneha Sant · Jamieson D. Ballan · Jasmine Kay · Luis E. Lara Gonzalez · Kylie Clarke · Han Xian Aw Yeang · Rejhan Idrizi · Metta Jana · Damon J. Challice · Roberto Salgado · Heather Thorne · David Amor · Lesley Andrews · Yoland Antill · Rosemary Balleine · Jonathan Beesley · Ian Bennett · Michael Bogwitz · Simon Bodek · Leon Botes · Meagan Brennan · Melissa Brown · Michael Buckley · Jo Burke · Phyllis Butow · Liz Caldon · Ian Campbell · Michelle Cao · Anannya Chakrabarti · Deepa Chauhan · Manisha Chauhan · Georgia Chenevix-Trench · Alice Christian · Paul Cohen · Alison Colley · Ashley Crook · James Cui · Eliza Courtney · Margaret Cummings · Sarah-Jane Dawson · Anna deFazio · Martin Delatycki · Rebecca Dickson · Joanne Dixon · Stacey Edwards · Gelareh Farshid · Andrew Fellows · Georgina Fenton · Michael Field · James Flanagan · Peter Fong · Laura Forrest · Stephen Fox · Juliet F
20 October 2025 at 08:00

Nature, Published online: 20 October 2025; doi:10.1038/s41586-025-09713-5

Parity and lactation induce T cell mediated breast cancer protection
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