AI News Archive: May 12, 2026 — Part 25
Sourced from 500+ daily AI sources, scored by relevance.
- Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models
Generating realistic and user-preferred advertisements is a key challenge in e-commerce. Existing approaches utilize multiple independent models driven by click-through-rate (CTR) to controllably create attractive image or text advertisements. However, their pipelines lack cross-modal perception and...
- From Trajectories to Phenotypes: Disease Progression as Structural Priors for Multi-organ Imaging Representation Learning
Imaging-derived phenotypes (IDPs) summarize multi-organ physiology but provide only static snapshots of diseases that evolve over time. In contrast, longitudinal electronic health records encode disease trajectories through temporal dependencies among past diagnosis events and comorbidity structure....
- RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems
The integration of Large Language Model (LLM) agents is transforming recommender systems from simple query-item matching towards deeply personalized and interactive recommendations. Reinforcement Learning (RL) provides an essential framework for the optimization of these agents in recommendation tas...
- Very Efficient Listwise Multimodal Reranking for Long Documents
Listwise reranking is a key yet computationally expensive component in vision-centric retrieval and multimodal retrieval-augmented generation (M-RAG) over long documents. While recent VLM-based rerankers achieve strong accuracy, their practicality is often limited by long visual-token sequences and ...
- Quality-Aware Collaborative Multi-Positive Contrastive Learning for Sequential Recommendation
The effectiveness of contrastive learning in sequential recommendation hinges on the construction of contrastive views, which ideally should be both semantically consistent and diverse. However, most existing CL-based methods rely on heuristic augmentations that are prone to removing crucial items o...
- HSUGA: LLM-Enhanced Recommendation with Hierarchical Semantic Understanding and Group-Aware Alignment
Large language model (LLM)-enhanced sequential recommendation typically aims to improve two core components: user semantic embedding extraction and utilization. Despite promising results, existing methods still have two limitations: 1) In the extraction stage, most methods directly input long intera...
- FedMM: Federated Collaborative Signal Quantization for Multi-Market CTR Prediction
Online platforms such as Amazon and Netflix serve users across multiple countries and regions, underscoring the importance of multi-market recommendation (MMR). Most MMR methods adopt a pre-training and fine-tuning paradigm, in which a unified model is first trained on centralized, global data and s...
- Test-Time Compute for Dense Retrieval: Agentic Program Generation with Frozen Embedding Models
Test-time compute is widely believed to benefit only large reasoning models. We show it also helps small embedding models. Most modern embedding checkpoints are distilled from large LLM backbones and inherit their representation space; a frozen embedding model should therefore benefit from extra inf...
- Caraman at SemEval-2026 Task 8: Three-Stage Multi-Turn Retrieval with Query Rewriting, Hybrid Search, and Cross-Encoder Reranking
We describe our system for SemEval-2026 Task 8 (MTRAGEval), participating in Task A (Retrieval) across four English-language domains. Our approach employs a three-stage pipeline: (1) query rewriting via a LoRA-fine-tuned Qwen 2.5 7B model that transforms context-dependent follow-up questions into st...
- TwiSTAR:Think Fast, Think Slow, Then Act,Generative Recommendation with Adaptive Reasoning
Generative recommendation with Semantic IDs (SIDs) has emerged as a promising paradigm, yet existing methods apply a fixed inference strategy, either fast direct generation or slow chain-of-thought reasoning, uniformly across all user histories. This approach creates a trade-off: fast recommendation...
- Conditional Memory Enhanced Item Representation for Generative Recommendation
Generative recommendation (GR) has emerged as a promising paradigm that predicts target items by autoregressively generating their semantic identifiers (SID). Most GR methods follow a quantization-representation-generation pipeline, first assigning each item a SID, then constructing input representa...
- Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence
During disasters, extracting causal relations from social media can strengthen situational awareness by identifying factors linked to casualties, physical damage, infrastructure disruption, and cascading impacts. However, disaster-related posts are often informal, fragmented, and context-dependent, ...
- Google finds first AI-developed zero-day that bypasses 2FA — self-morphing malware and Gemini-powered backdoors signal a new era of cybercrime
Google cybersecurity boffins found at least one AI-developed zero-day exploit
- Environmental Volatility Shifts Visual Search from Capture to Caution
Real-world distractors occur in environments whose states change at different rates. We asked whether such volatility alters early attentional gating or instead changes the criterion for committing to a response. Observers performed an additional-singleton search task with concurrent eye tracking while distractor presence followed high- or low-volatility sequences, with overall distractor prevalence held constant. Trial-pooled oculomotor capture was higher under high volatility, a pattern that appears to indicate altered filtering. That inference did not survive repetition-aware analysis: once the same-location run position was matched, capture did not detectably differ across volatility regimes. The pooled capture effect was therefore consistent with a structural consequence of the volatility manipulation, which enriched high-volatility blocks with early-run positions where capture is intrinsically high. The positive volatility signature appeared on distractor-absent trials, where hig
- A computational model reveals that spatial localization of cancer stem cells increases radioresistance in tumorspheres
Cancer stem cells (CSCs) exhibit increased resistance to radiotherapy, contributing to tumor recurrence and progression. While CSCs are known for their intrinsic resistance, the role of their spatial organization remains poorly understood. We extend a computational model of tumorsphere growth to investigate how the spatial distribution of CSCs influences radiation response. The model explicitly tracks cell lineages and spatial positions, revealing a preferential accumulation of CSCs in the spheroid interior. Because radiosensitivity increases with oxygen availability, and oxygen levels are lowest in the tumor core, this spatial organization confers a protective advantage to the CSC population. We find that this effect is negligible in small, well-oxygenated tumorspheres but becomes pronounced as growth leads to the emergence of hypoxic regions. To isolate the role of spatial structure, we compare these results with control simulations in which CSC positions are randomly reassigned. In
- Spurious correlation inflates performance in single-cell perturbation prediction
The increasing number of computational methods designed to predict the effects of genetic perturbations on cellular gene expression profiles has led to a need for rigorous evaluation metrics. Recent benchmarking studies rely on correlation or cosine similarity of differential expression relative to a shared population of control cells. We show that these metrics are systematically inflated by statistical bias induced by reusing the same control population to define both quantities being compared. As a result, even non-informative methods can appear to perform well, particularly in datasets with limited numbers of control cells. Reanalysis of published datasets using a simple control-splitting procedure that removes this bias leads to a substantial reduction in performance previously attributed to biological signal.
- Temporal-deviation-driven community detection uncovers early-warning signals for critical transitions in complex diseases
Early detection of critical transitions in complex diseases is crucial for timely clinical intervention. However, as patients often provide only a single snapshot, identifying sample-specific early-warning signals (EWS) from a dynamical evolution perspective remains challenging, coupled with high-dimensional noise amplification. Here, we present TD-COM, a framework for detecting personalized EWS of critical transitions via single-sample community detection. By constructing a temporal perturbation map STDN, TD-COM captures latent dynamical perturbations inferred from static individual profiles. Synergizing these temporal-deviation signals with static topological features, TD-COM implements a multi-level node filtering strategy during community detection, effectively suppressing single-sample noise. Validated on hour-scale, multi-year, and multi-decade transcriptomic data, TD-COM robustly detects critical states preceding clinical deterioration and uncovers their underlying molecular mec
- Dual-view Guided Context-aware Network for Automated Bone Lesion Segmentation and Quantification in Whole-body SPECT
Whole-body SPECT bone scintigraphy reflects skeletal metabolic activity throughout the body and plays an indispensable role in the screening, treatment evaluation, and prognostic assessment of bone metastases in tumors. However, the automatic detection and segmentation of hypermetabolic bone lesions remain challenging due to low contrast, limited spatial resolution, and complex lesion distributions. In this study, we proposed Bone-Segnet, a dual-view guided automatic segmentation network for hypermetabolic bone lesions that integrated multi-scale feature modeling, global context modeling, and view-conditioned modulation. Pixel-level annotated anterior and posterior whole-body bone scintigraphy images were used for model training and prediction. The proposed network enhanced the recognition of low-contrast and small-scale lesions through small-lesion enhancement and multi-scale contextual modeling. A Transformer module was further introduced to strengthen global feature representation,
- A Three-Layered Agent-Based Model of Adult Hippocampal Neurogenesis (HANG-AB3L) with Stochastic Cell Fate Determination
Hippocampal adult neurogenesis (HANG) is a highly regulated process where neural stem cells progress through distinct stages, from Type 1 radial glia-like cells to mature neurons, via a complex series of proliferative and differentiative divisions. While recent in vivo imaging has challenged the classical paradigm of asymmetric division, the exact relationship between individual cell-fate decisions and long-term population stability remains difficult to quantify empirically. In this study, we utilized an agent-based (AB) model to simulate the stochastic dynamics of the hippocampal neurogenic niche. Our results demonstrate that while individual progenitor lineages exhibit high variability and probabilistic division symmetries (proliferative symmetric, asymmetric, and differentiative symmetric), the system achieves deterministic stability as the initial progenitor density increases. We found that the Type 1 progenitor pool follows a negative exponential decay profile, with its longevity
- Musk Lawyer’s Question for Sam Altman on the Stand: Are You Trustworthy?
Mr. Altman, the C.E.O. of OpenAI, said on Tuesday that he worried Elon Musk wanted control of the A.I. lab.
- A Multimodal Framework for Organ- and Cell-Resolved Biological Aging and Longevity Intervention Discovery
Aging is the primary driver of chronic disease and mortality, requiring comprehensive frameworks for quantification of aging and nomination of longevity interventions. We developed mAge (multimodal age), a biological aging framework that integrates plasma proteomics, wearables, and mortality hazard to predict biological age, intrinsic capacity, and mortality risk. By combining proteomic and wearable data in UK Biobank samples, mAge exceeds unimodal baseline age prediction to 0.87 test R2 and 2.3 years mean error, and reduces unimodal baseline mortality prediction error by 21%. We further constructed organ- and cell type-specific biological clocks that quantify aging across 49 distinct subsystems, revealing that cardiac, immune, and intracellular protein signatures benefit most from wearable integration. By mapping data to FDA-approved drug targets, we identified interventions, such as GLP-1 receptor agonists, gabapentin, and ACE inhibitors, that are associated with lower overall and su
- Evaluating Genomic Surveillance Methods for Shigella sonnei in a High-Income Setting
Shigella sonnei is a human-adapted enteric pathogen with a very low infectious dose and increasing antimicrobial resistance. In high-income settings, transmission is multimodal including sporadic cases/outbreaks associated with food and travel, as well as sustained transmission among sexual networks of men who have sex with men (MSM). Whole-genome sequencing (WGS) now underpins national shigellosis surveillance in the United Kingdom. Hence, consistent, communicable genotyping is essential for case linkage and trend detection across heterogeneous transmission modes. Here, we evaluate the performance of WGS genotyping approaches for granulating outbreaks of S. sonnei shigellosis, particularly considering differential performance in dense sexual transmission where highly clonal MSM-associated sublineages pose distinct clustering challenges. Specifically, we compare performance of the current practice approach (10 SNP-distance clustering based on SNP address [t10]), allele-based methods (E
- Development and Validation of a Multimodal Clinical, Pathologic, and Genomic Model for Breast Cancer Recurrence
Purpose: To develop and validate a multimodal recurrence-risk model integrating histology, genomic testing, and clinical variables. Methods: We developed AI-Path, a whole-slide image biomarker for recurrence prediction trained in CALGB 9344, and validated it in three independent cohorts: TAILORx, a multi-site Chicago cohort, and the MDX-BRCA cohort. We then integrated AI-Path with Oncotype DX Recurrence Score (RS), tumor size, and nodal status into a Cox model, PathClinRS, fit using 60% of cases from TAILORx, with the remaining 40% held out for validation. The primary end point was distant recurrence-free interval. Performance was assessed using Harrell's concordance index (C-index) and Kaplan-Meier analyses. Results: A total of 12,418 patients were included. In TAILORx, AI-Path outperformed RS for distant recurrence (C-index, 0.682 vs 0.647; P = .038), driven by superior prediction of late recurrence (0.656 vs 0.567; P < .001). In node-negative disease, PathClinRS outperformed RSClin
- Characterization of menopause onset and associated disease risks using large-scale electronic health records
Menopause affects over one billion women worldwide, yet remains poorly characterized at scale. We apply an ICD-10-based phenotyping algorithm to electronic health records (EHR) from an academic medical center (n=33,444 women aged 35-64) and a safety-net hospital system (n=7,041), yielding one of the most racially and socioeconomically diverse menopause cohorts in the literature. Structured EHR fields underrepresent symptom burden: only 38.8% of patients had any documented symptom via natural language processing, despite an estimated prevalence of 90%. Adverse pregnancy outcomes were associated with earlier menopause onset after adjustment (beta=-1.12 years, p=8.7x10^-45). Menopausal women showed elevated risk for osteoporosis (hazard ratio of 12.40), rheumatoid arthritis (HR of 2.43), and mental and behavioral disorders (HR of 2.38) relative to age-matched men, with divergence at menopause onset. We show that large-scale EHR can characterize menopause at a scale and diversity that pros
- Machines with the ability to ‘feel’ currently in development as we enter next frontier of AI
Machines with the ability to ‘feel’ currently in development as we enter next frontier of AI EurekAlert!
- 60% of U.S. teens have tried AI chatbots, 11.4% use them almost daily
60% of U.S. teens have tried AI chatbots, 11.4% use them almost daily EurekAlert!
- Smart AI gives electric vehicle batteries 23 per cent longer life – without increasing the charging time
Smart AI gives electric vehicle batteries 23 per cent longer life – without increasing the charging time EurekAlert!
- Smart AI charging can extend electric car battery life (IMAGE)
Smart AI charging can extend electric car battery life (IMAGE) EurekAlert!
- AI content moderation takes a lesson from economics #ASA190
AI content moderation takes a lesson from economics #ASA190 EurekAlert!
- Design tweaks promote responsible AI use for environmental protection, research shows
Design tweaks promote responsible AI use for environmental protection, research shows EurekAlert!
- Button‑pushing explorers: How to grasp that AI agents can do amazing things while knowing nothing
The nonprofit ARC Prize Foundation on May 1, 2026, released the results of a new benchmark: a test of an AI system's ability to solve a game. The results were striking—humans scored 100%, while the most advanced AI systems scored under 1%.
- Apple Sales Coach Will Use AI-Generated Video Presenters
The Apple Sales Coach app will begin using AI-generated video presenters to deliver personalized training content to retail salespeople around the world. In a new video message, an Apple trainer said that the update addresses a limitation of traditional training programs: the impossibility of creating truly individualized content for hundreds of thousands of salespeople across different markets, languages, and product focuses. Apple said it will now use AI to generate short, focused videos tailored to the products a seller works with, the skills they are developing, and the language they speak. Apple to Use AI-Generated Presenters for Sales Training Videos pic.twitter.com/6DRkLAvyfm — Aaron (@aaronp613) May 12, 2026 AI-generated presenters will be identifiable by an on-screen icon, and Apple emphasized that the underlying content remains entirely human-driven. The company's training team apparently writes every script and verifies every detail, with AI serving as the delivery mechanism
- Rivian’s AI-powered voice assistant is ready to roll
The Rivian Assistant can answer questions about the vehicle itself, or interact with and modify the driver’s personal apps, like Google Calendar.
- An Easy Guide to Creating a Custom AI Assistant
An Easy Guide to Creating a Custom AI Assistant entrepreneur.com
- Copper prices are at their highest level on record. AI is only part of the story
Copper prices are at their highest level on record. AI is only part of the story
- ‘It would be insane’ for spy agencies to not have AI model early access, lawmaker says
The top Democrat on the House Intelligence Committee said the Commerce Department should also have a role in AI policy.
- Family of FSU shooting victim sues OpenAI, alleging ChatGPT helped gunman plan attack that killed 2
Florida's attorney general launched a criminal investigation into OpenAI's involvement in the shooting, saying ChatGPT offered significant advice to the shooter before he committed the crimes. The company says it proactively shared information with law enforcement.
- Family of FSU Mass Shooting Victim Sues OpenAI
The family of a man killed in a 2025 mass shooting at Florida State University has filed a lawsuit against OpenAI in a U.S. court, claiming the shooter was aided by ChatGPT in planning the attack. The family of Tiru …
- Nvidia CEO Jensen Huang Has a Surprising Message for Graduates Worried AI Will Take Their Jobs
Nvidia CEO Jensen Huang Has a Surprising Message for Graduates Worried AI Will Take Their Jobs entrepreneur.com
- Half-tonne Transformers-style robot capable of smashing concrete unveiled in China
A Chinese company has unveiled the world's first Transformers-style "mecha" robot weighing half a tonne.
- Exaforce Raises $125 Million for Agentic SOC Platform
Exaforce has raised a total of $200 million and plans on using the latest investment for product development and international expansion. The post Exaforce Raises $125 Million for Agentic SOC Platform appeared first on SecurityWeek .
- Threads users are pissed they can't block Meta's new AI chatbot
It's the top trending topic on the platform.
- Abu Dhabi's Phoenix Group partners with DC Max to unlock $8 billion European AI Data Center opportunity, with Lyon, France as first deployment
Abu Dhabi's Phoenix Group partners with DC Max to unlock $8 billion European AI Data Center opportunity, with Lyon, France as first deployment Gulf News
- Dubai updates driving licence tests: RTA introduces collision warning, lane assist
Dubai updates driving licence tests: RTA introduces collision warning, lane assist Gulf News
- UAE launches AI Cyber Factory to fight cyberattacks
UAE launches AI Cyber Factory to fight cyberattacks Gulf News
- AI minister announces 44 projects getting federal money to access compute power
AI minister announces 44 projects getting federal money to access compute power CBC
- Wall Street falls from its records as AI stocks slump and oil prices rise
Wall Street falls from its records as AI stocks slump and oil prices rise Boston Herald
- Wall Street falls from its records as AI stocks slump and oil prices rise
Wall Street falls from its records as AI stocks slump and oil prices rise Austin American-Statesman
- Wall Street falls from its records as AI stocks slump and oil prices rise
Wall Street falls from its records as AI stocks slump and oil prices rise Dallas News
- Wall Street’s record-setting run halts as AI stocks slump and oil prices rise
A sudden halt for technology stocks put the brakes on Wall Street's record-setting run.