AI News Archive: May 18, 2026 — Part 12
Sourced from 500+ daily AI sources, scored by relevance.
- Text-Video Retrieval With Global-Local Contrastive Consistency Learning
Text-video retrieval aims to find the most semantically similar videos with given text queries. However, since videos contain more diverse content than texts, the main semantics expressed by each text-video pair is often partially relevant. The primary methods involve the utilization of language-vid...
- DADF: A Distribution-Aware Debiasing Framework for Watch-Time Regression in Recommender Systems
Watch-time prediction is a central regression task in short-video recommender systems, where labels are highly long-tailed and residual errors vary systematically across observed watch-time regions. In practice, a model may appear globally calibrated while still overestimating short views and undere...
- Uncertainty-Calibrated Recommendations for Low-Active Users
A fundamental challenge in recommender systems is balancing reliability for Low-Active Users (LAUs) with diversity for High-Active Users (HAUs). The key to this balance lies in quantifying model uncertainty, which approximates the risk of prediction errors and reveals the limits of the model's curre...
- Transcript architecture predetermines m6A remodeling and sensory neuron vulnerability in chemotherapy-induced peripheral neuropathy
Whether individual transcripts carry intrinsic features that predetermine their response to external perturbations is unknown. Here we used nanopore direct RNA sequencing of male mouse dorsal root ganglia (DRG) to simultaneously profile N6-methyladenosine (m6A) modifications, poly(A) tail dynamics, and full-length isoform identity on individual RNA molecules from mice treated with bortezomib, a proteasome inhibitor that causes painful peripheral neuropathy. Machine learning revealed that transcript-intrinsic features predetermine the magnitude of perturbation-induced m6A loss (R2 = 0.983). Motif composition and spatial distribution alone, without any modification measurement, predicted 80-88% of m6A erosion variance, establishing that perturbation response is encoded in transcript architecture before any modification is deposited. Expression level contributed just 2.6% of predictive importance. Bortezomib removed a fixed ~73.5% fraction of m6A marks, meaning absolute loss scaled linear
- Structure and Dynamics of the HIV-1 Envelope Protein on the Virion Envelope
HIV-1 buds from infected cells as immature virion particles with a scattered envelope glycoprotein (Env) distribution on their envelope. It then undergoes maturation, during which the viral protease cleaves the Gag polyprotein at multiple sites, leading to structural reorganization of the viral particle and lateral redistribution of Env proteins, ultimately rendering the virion infectious. However, the underlying mechanism of maturation-induced Env reorganization remains elusive. In this study, we combine microsecond-long all-atom (AA), bottom-up coarse-grained (CG) molecular dynamics simulations, and diffusion model-based backmapping to investigate the structural organization and key interactions of Env in viral membranes. AA simulations of fully glycosylated Env embedded in HIV-1 mimetic asymmetric bilayers were first performed to characterize its conformational dynamics and Env-lipid interactions. We then developed a bottom-up CG model of glycosylated Env from that AA data and simul
- Deep Learning Structural Ensembles as Proxies for Protein Flexibility
Protein dynamics are essential to biological function, yet understanding whether deep learning models contain information about these dynamics remains an open question. In this study, we quantitatively investigate the capacity of deep learning structure generation methods to predict protein flexibilities by directly comparing residue-level mean squared fluctuation (MSF) profiles derived from structural ensembles with experimental or simulation-informed flexibility profiles. We assembled four diverse benchmark datasets representing different types of structural information, including 70 NMR ensembles, 43 X-ray crystallographic protein pairs in two distinct conformational states, 82 high-resolution cryo-EM structures, and molecular dynamics simulations of 10 proteins. Utilizing AlphaFold3, AlphaFold2, and RosettaFold to generate multiple structural models, we applied ranksort normalization to place the profiles on a comparable scale and quantified similarity primarily using cosine and Pe
- Factors Influencing Vitamin D Status in Guiyang, China: A Random Forest and SHAP Analysis
Objective To assess serum 25-hydroxyvitamin D [25(OH)D] levels in a health examination population in Guiyang, a low-latitude, high-altitude, and cloudy city in southwestern China, and to identify key determinants using machine learning. Methods This retrospective study included 10,931 adults (>20 years) who underwent health checkups at Guiyang First People's Hospital between February 2019 and April 2025. Beyond conventional statistical comparisons, a two-stage machine learning approach was applied: LASSO regression for feature selection, followed by an optimized Random Forest regression model (mtry = 2). SHapley Additive exPlanations (SHAP) were used to quantify variable importance. Results The median serum 25(OH)D level was 36.63 (IQR 24.77,53.17) nmol/L. Vitamin D deficiency (<50 nmol/L) was present in 70.98% of participants, while sufficiency (>75 nmol/L) was only 7.35%. Significantly lower levels were observed in females, in adults aged <30 years (deficiency rate 85.6%), and during
- Assessing the reliability of immunofluorescence image analysis with artificial intelligence
In view of the outstanding progress of machine learning (ML) and growing cost of health systems, it is a current challenge to incorporate artificial intelligence tools into actual medical practice. Here we explored the feasibility and reliability of using machine learning to perform an important immunological investigation that currently requires experienced biologists : Anti-nuclear cytoplasmic antibodies (ANCAs) are important markers for vasculitis and they may be evidenced by microscopic examination of cells labeled with patients' sera. The use of a reliable ML classifier to discriminate between positive and negative samples would increase the rapidity and decrease the cost of immunofluorescence-based ANCA detection. Here, we tested seven well-documented ML algorithms, ranging from simple models such as k nearest neighbors to more complex convolutional neural networks involving millions of adjustable parameter. We studied the feasibility and reliability of classifying 1114 serum sam
- Large Language Model Performance in UK Advice & Guidance: A Pilot Study in Neurology
Background: Large language models (LLMs) demonstrate strong performance in controlled medical environments such as multiple choice exams, but their utility in real-world clinical workflows remains unproven. The NHS Advice & Guidance (A&G) service, where Primary Care clinicians can submit text-based queries to specialists, provides an environment for evaluating the clinical performance of LLMs as a specialist. Methods: We compared responses from MedGemma 4B-IT, an open-weight model deployed locally on hospital infrastructure, against specialist neurologist responses across 50 adult neurology A&G cases from University College London Hospital. Two neurologists and two GPs rated 80 blinded and 20 unblinded responses for outcome, safety, efficacy, and feasibility using standardised criteria; outcome was a binary correct/incorrect, while other domains were scored 1-5. Inter-rater reliability was assessed using intraclass correlation coefficients. Results: Although there were no statistically
- A clinically integrated, frameless human Neuropixels workflow
High-density electrophysiological recording using Neuropixels probes enables single-unit resolution of human neural activity. However, integrating these systems into clinical environments remains challenging. Reported human recordings have been limited to a few centres in the United States utilising variable regulatory, sterilisation and operative techniques. Here, we present human Neuropixels recordings under a nationally managed ethical and regulatory framework in the United Kingdom. We provide a reproducible roadmap to overcome regulatory and equipment constraints. Guided by the IDEAL Stage 2a (Development) framework, we established a frameless intraoperative workflow utilising manufacturer-sterilised probes and a commercially available, clinical-grade setup for Neuropixels insertion including micromanipulator and endoscope holder. We prospectively evaluated this workflow across six participants (mean age 62.5 years) undergoing elective ventriculoperitoneal shunt surgery. Iterative
- Wearable EEG during gameplay captures a robust P300 cognitive signal in unsupervised home settings
Objective. Continuous, unsupervised monitoring of cognitive brain responses has long been constrained by the demands of laboratory EEG. Whether the P300 event-related potential, an established marker of attention and cognitive processing, can be elicited as an incidental byproduct of genuine gameplay, recorded with a minimal wearable EEG system under unsupervised home conditions, has not been established. Approach. Ten healthy adults played a gamified visual oddball task in which infrequent target stimuli (green gates) were embedded among frequent non-targets (red gates) within a continuous third-person running game. EEG was recorded with a four-channel dry-electrode headband (EEG channels: O1, O2, T3, T4; forehead reference; 250Hz) with self-mounted electrodes in a home setting, without experimenter supervision. Group-level effects were assessed with cluster-based permutation tests and peak-amplitude tests. Single-trial classification used linear discriminant analysis (LDA) with four
- Clinical Note Comparison and Data Retrieval Via Embedding Vectors: Model Selection, Metrics, and Convergence
Background: Embedding models are an integral part of generative AI architectures, transforming text into embedding vectors that represent semantic content in numerical form. Despite their central role, their performance in clinical settings remains underexplored. We evaluate embedding models across two tasks: semantic difference detection in clinical texts, and data retrieval from patient records. Methods: Eight models were applied to synthetic discharge summaries in English, Finnish, and Swedish. Semantic sensitivity was assessed by introducing controlled perturbations (deletion, modification, and paraphrasing) at three levels of severity; cosine similarity, and L1 and Euclidean distances were computed between the vectors of the original and perturbed texts. Partial vectors were compared to explore dimensionality reduction. Two models with the biggest contrast in semantic difference detection were evaluated on retrieval of relevant information from real Finnish vascular surgery record
- Improved prostate cancer prediction by combining Prostate-Specific Antigen (PSA) test results with Genetic Risk Scores (GRS/PRS)
Background: Prostate cancer is the second most common cancer in men worldwide. The Prostate Specific Antigen (PSA) blood test is widely used for prostate cancer detection but suffers from high false-positive rates (up to 80%). Genetic risk scores (GRS/PRS) have a similar performance to PSA testing in predicting prostate cancer risk. Method: GRS269 for prostate cancer was derived using 269 known risk variants and applied to UK Biobank participants. We assessed whether GRS269 improved power to predict prostate cancer diagnosis on top of age and pre-prostatectomy PSA level among 17,380 cases. Longitudinal PSA measurements were processed as median, first, last (most recent), and random PSA. All models were adjusted for age. Results: Across all PSA measures, the integrated model combining GRS269, PSA, and age consistently outperformed models using GRS269 or PSA alone. The highest predictive performance was observed using the last PSA value combined with GRS269 (AUC = 0.82, 95% CI: 0.81-0.82
- NextEra’s $67 billion deal pokes the AI bear
Regulators’ approval will test Americans’ anger against AI.
- The US megadeal set to spark a fight over the cost of the AI boom
Proposed deal between NextEra and Dominion would cement control of US ‘data centre alley’
- Swamped by data center demands, Dominion Energy just opted for a megamerger
Swamped by data center demands, Dominion Energy just opted for a megamerger The Washington Post
- Deal Creates One Of World's Largest Utilities, Major Power Provider For AI Data Centers
Both companies are trying to meet surging power demand for data centers. The post Deal Creates One Of World's Largest Utilities, Major Power Provider For AI Data Centers appeared first on Investor's Business Daily .
- The fate of OpenAI’s $1tn IPO will be decided in an Oakland jury room
Elon Musk’s legal challenge could derail the AI start-up’s commercial ambitions
- Musk To Appeal OpenAI Verdict: Lawyer Says 'War' Is 'Not Over'
The jury unanimously found Musk sued OpenAI and Sam Altman after the statute of limitations had expired, tossing his claims.
- Sigma Computing seals $80M funding round as it pivots toward ‘agentic analytics’
Cloud-native data analytics startup Sigma Computing Inc. has closed on an $80 million Series E funding round that doubles its valuation to $3 billion, almost a year to the day after its previous Series D raise. Today’s round was led by Princeville Capital and saw participation from new investors Databricks Ventures, ServiceNow Ventures and Workday […] The post Sigma Computing seals $80M funding round as it pivots toward ‘agentic analytics’ appeared first on SiliconANGLE .
- TRACE: Trajectory Correction from Cross-layer Evidence for Hallucination Reduction
Hallucination correction is not a one-direction problem. We show that intermediate layers are neither uniformly more truthful than final layers nor uniformly less trustworthy. Yet hallucination reduction is usually instantiated through one fixed intervention form: contrast one layer against another,...
- FOL2NS: Generating Natural Sentences from First-Order Logic
Translating formal language into natural language is a foundational challenge in NLP, driving various downstream applications in semantic parsing, theorem validation, and question answering. In this study, we introduce First-Order Logic to Natural Sentence (FOL2NS), a neurosymbolic framework designe...
- iPOE: Interpretable Prompt Optimization via Explanations
Prompt optimization has often been framed as a discrete search problem to find high-performing and robust instructions for an LLM. However, the search result might not make it transparent why and where specific prompt changes lead to performance gains. This is in contrast to how humans are instructe...
- How Good LLMs Are at Answering Bangla Medical Visual Questions? Dataset and Benchmarking
Recent advancements in Large Language Models (LLMs) and Large Vision Language Models (LVLMs) have enabled general-purpose systems to demonstrate promising capabilities in complex reasoning tasks, including those in the medical domain. Medical Visual Question Answering (MedVQA) has particularly benef...
- A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM$Δ$ Integration into Upcycled MoE
Expanding Large Language Models~(LLMs) to new languages is a costly endeavor, demanding extensive Continued Pre-Training~(CPT) and data-intensive alignment. While recent data-free merging techniques attempt to bypass alignment by fusing a multilingual CPT-enhanced model with its instruct counterpart...
- MA$^{2}$P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion
Persuasive dialogue generation plays a vital role in decision-making, negotiation, counseling, and behavior change, yet it remains a challenging problem. In complex persuasion where the persuadee's internal states are not expressed clearly, the persuader must interpret responses, infer the persuadee...
- Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics
Large Reasoning Models (LRMs) introduce new opportunities for safety monitoring through their Chain of Thought (CoT) reasoning. However, CoT is not always faithful to the model's final output, undermining its reliability as a monitoring tool. To address this, we investigate the hidden representation...
- What is Holding Back Latent Visual Reasoning?
Humans can approach complex visual problems by mentally simulating intermediate visual steps, rather than reasoning through language alone. Inspired by this, several works on Vision-Language Models have recently explored chain-of-thought reasoning with continuous latent tokens as intermediate visual...
- Machine Unlearning for Masked Diffusion Language Models
Recent masked diffusion language models (MDLMs), such as LLaDA and Dream, have achieved performance comparable to autoregressive large language models. Unlike autoregressive models, which generate text sequentially, MDLMs generate text by iteratively denoising masked positions in parallel. During fi...
- SIREM: Speech-Informed MRI Reconstruction with Learned Sampling
Real-time magnetic resonance imaging (rtMRI) of speech production enables non-invasive visualization of dynamic vocal-tract motion and is valuable for speech science and clinical assessment. However, rtMRI is fundamentally constrained by trade-offs among spatial resolution, temporal resolution, and ...
- Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction
We introduce Graph-Augmented Sequence-to-Sequence (GA-S2S), a novel framework that integrates a T5-small encoder-decoder with a Relational Graph Attention Network (RGAT) to improve link prediction in knowledge graphs. While existing Seq2Seq models rely solely on surface-level textual descriptions of...
- Scalable Environments Drive Generalizable Agents
Generalizable agents should adapt to diverse tasks and unseen environments beyond their training distribution. This position paper argues that such generalization requires environment scaling: expanding the distribution of executable rule-sets that agents interact with, rather than only increasing t...
- SafeDiffusion-R1: Online Reward Steering for Safe Diffusion Post-Training
Diffusion models have been widely studied for removing unsafe content learned during pre-training. Existing methods require expensive supervised data, either unsafe-text paired with safe-image groundtruth or negative/positive image pairs, making them impractical to scale. Furthermore, offline reinfo...
- MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents
Recent GUI agents have made substantial progress in visual grounding and action prediction, yet they remain brittle in long-horizon tasks that require maintaining task state across many interface transitions. Existing agents typically rely on raw history replay or text-only memory, which either over...
- FlintLab Sirius Platform
AI-Native Unified Device Infra PaaS
- Shallow ReLU$^s$ Networks in $L^p$-Type and Sobolev Spaces: Approximation and Path-Norm Controlled Generalization
We study approximation by shallow ReLU$^s$ networks, $σ_s(t)=\max{0,t}^s$, and the generalization behavior of such networks under $\ell_1$ path-norm control. For the $L^p$-type integral spaces $\widetilde{\mathcal{F}}_{p,τ_d,s}$, $1\le p\le2$, we establish approximation bounds for shallow networks u...
- Improved Baselines with Representation Autoencoders
Representation Autoencoders (RAE) replace traditional VAE with pretrained vision encoders. In this paper, we systematically investigate several design choices and find three insights which simplify and improve RAE. First, we study a generalized formulation where the representation is defined as sum ...
- Anthropic has acquired the dev tools startup used by OpenAI, Google, and Cloudflare
Stainless, a New York-based startup, founded in 2022, rose to prominence in the emerging AI industry for automating the creation and maintenance of software development kits, or SDKs — the libraries developers use to interact with APIs.
- Musk loses OpenAI court battle after jury finds he waited too long to sue
Jurors spent weeks hearing about Musk's claim that Altman had "stolen a charity."
- Code as Agent Harness
Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineering. In emerging agentic systems, code is no longer only a target output. It increasingly serves as an operational substra...
- Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation
Multimodal Large Language Models (MLLMs) still struggle with fine-grained visual understanding, where answers often depend on small but decisive evidence in the full image. We observe a regional-to-global perception gap: the same MLLM answers fine-grained questions more accurately when conditioned o...
- Predictable Confabulations: Factual Recall by LLMs Scales with Model Size and Topic Frequency
While scaling laws govern aggregate large language model performance, no scaling law has linked factual recall to both model size and training-data composition. We evaluated 38 models on over 8,900 scholarly references evaluated by an automated reference verification system. Recall quality follows a...
- Semantic Generative Tuning for Unified Multimodal Models
Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture. However, prevailing training paradigms independently optimize understanding via sparse text signals and generation through dense pixel objectives. Such a decoupled strategy...
- Federated Martingale Posterior Samping
Federated Bayesian neural networks require fixing a prior on the model parameters together with a likelihood. Eliciting meaningful priors on the weight space of modern overparameterized models is notoriously difficult, and misspecification of either component can severely degrade accuracy and calibr...
- Distilling Tabular Foundation Models for Structured Health Data
Tabular foundation models (TFMs) achieve strong performance on health datasets, but their inference cost and infrastructure requirements limit practical use. We study whether their predictive behavior can be transferred to lightweight tabular models through knowledge distillation. Since in-context T...
- SkillGenBench: Benchmarking Skill Generation Pipelines for LLM Agents
As LLM agents are increasingly built around reusable skills, a central challenge is no longer only whether agents can use provided skills, but whether they can generate correct, reusable, and executable skills from repositories and documents. Existing benchmarks primarily evaluate the efficacy of gi...
- Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches
Optimization models developed by operations research (OR) experts are often deployed as decision-support systems in industrial settings. However, real-world environments are dynamic, with evolving business rules, previously overlooked constraints, and unforeseen perturbations. In such contexts, end ...
- Learning Quantifiable Visual Explanations Without Ground-Truth
Explainable AI (XAI) techniques are increasingly important for the validation and responsible use of modern deep learning models, but are difficult to evaluate due to the lack of good ground-truth to compare against. We propose a framework that serves as a quantifiable metric for the quality of XAI ...
- Lance: Unified Multimodal Modeling by Multi-Task Synergy
We present Lance, a lightweight native unified model supporting multimodal understanding, generation, and editing for both images and videos. Rather than relying on model capacity scaling or text-image-dominant designs, Lance explores a practical paradigm for unified multimodal modeling via collabor...
- COOPO: Cyclic Offline-Online Policy Optimization Algorithm
Offline reinforcement learning struggles with distributional shift and constrained performance due to static dataset limitations, while online RL demands prohibitive environment interactions. The recent advent of hybrid offline-to-online methods bridges these domains but suffers from distribution dr...