AI News Archive: April 29, 2026 — Part 16

Sourced from 500+ daily AI sources, scored by relevance.

• Split over $n$ resource sharing problem: Are fewer capable agents better than many simpler ones?

  In multi-agent systems, should limited resources be concentrated into a few capable agents or distributed among many simpler ones? This work formulates the split over $n$ resource sharing problem where a group of $n$ agents equally shares a common resource (e.g., monetary budget, computational resou...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26374v1
• When Agents Shop for You: Role Coherence in AI-Mediated Markets

  Consumers are increasingly delegating purchase decisions to AI agents, providing natural-language descriptions of their preferences and identity. We argue that these representations constitute an information channel, role coherence, through which sellers can infer willingness to pay without explicit...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26220v1
• MultEval: Supporting Collaborative Alignment for LLM-as-a-Judge Evaluation Criteria

  LLM-as-a-judge approaches have emerged as a scalable solution for evaluating model behaviors, yet they rely on evaluation criteria often created by a single individual, embedding that person's assumptions, priorities, and interpretive lens. In practice, defining such criteria is a collaborative and ...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26679v1
• FASH-iCNN: Making Editorial Fashion Identity Inspectable Through Multimodal CNN Probing

  Fashion AI systems routinely encode the aesthetic logic of specific houses, editors, and historical moments without disclosing it. We present FASH-iCNN, a multimodal system trained on 87,547 Vogue runway images across 15 fashion houses spanning 1991-2024 that makes this cultural logic inspectable. G...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26186v1
• Transferability of Token Usage Rights: A Design Space Analysis of Generative AI Services

  With the rapid spread of generative AI services, the token has gained value not only as a technical unit of language processing but also as an economic currency for accessing AI services. Major AI model providers have adopted token-based billing as their default service model, requiring users to pur...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26683v1
• Persona-Based Process Design for Assistive Human-Robot Workplaces for Persons with Disabilities

  Human-robot interaction is emerging as an important paradigm for integrating persons with disabilities into the workplace. While these systems can enable individuals to work, their design is mostly personalized, hindering widespread use beyond the individual user. The universal design paradigm is a ...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26527v1
• UIGaze: How Closely Can VLMs Approximate Human Visual Attention on User Interfaces?

  Vision Language Models (VLMs) have demonstrated strong capabilities in understanding visual content, yet their ability to predict where humans look on user interfaces remains unexplored. We present UIGaze, a study investigating how closely VLMs can approximate human visual attention on user interfac...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26352v1
• Exploring the Feasibility and Acceptability of AI-Mediated Serious Illness Conversations in the Emergency Department

  Serious illness conversations (SICs) align care with patients' values, goals, and preferences, yet they rarely occur in emergency departments (EDs), where time constraints and emotional burden often leave clinicians making high-stakes decisions without documented insight into what matters most to pa...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26214v1
• PRAG End-to-End Privacy-Preserving Retrieval-Augmented Generation

  Retrieval-Augmented Generation (RAG) is essential for enhancing Large Language Models (LLMs) with external knowledge, but its reliance on cloud environments exposes sensitive data to privacy risks. Existing privacy-preserving solutions often sacrifice retrieval quality due to noise injection or only...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26525v1
• Differentially Private Contrastive Learning via Bounding Group-level Contribution

  Differentially private (DP) contrastive learning aims to learn general-purpose representations from sensitive data, alleviating the privacy leakage concerns of organizations deploying or sharing embedding models trained on private user content. However, existing approaches suffer from severe utility...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26467v1
• VulStyle: A Multi-Modal Pre-Training for Code Stylometry-Augmented Vulnerability Detection

  We present VulStyle, a multi-modal software vulnerability detection model that jointly encodes function-level source code, non-terminal Abstract Syntax Tree (AST) structure, and code stylometry (CStyle) features. Prior work in code representation primarily leverages token-level models or full AST tr...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26313v1
• eDySec: A Deep Learning-based Explainable Dynamic Analysis Framework for Detecting Malicious Packages in PyPI Ecosystem

  The security of open-source software repositories is increasingly threatened by next-gen software supply chain attacks. These attacks include multiphase malware execution, remote access activation, and dynamic payload generation. Traditional Machine Learning (ML) detectors struggle to detect these a...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26219v1
• OpenSOC-AI: Democratizing Security Operations with Parameter Efficient LLM Log Analysis

  Small and medium sized businesses (SMBs) face an escalating cybersecurity threat landscape, yet most lack the resources to staff full Security Operations Centers (SOCs) or deploy enterprise grade detection platforms. This paper presents OpenSOC-AI, a lightweight log analysis framework that uses para...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26217v1
• Privacy-Preserving Clothing Classification using Vision Transformer for Thermal Comfort Estimation

  A privacy-preserving clothing classification scheme is presented to enable secure occupant-centric control (OCC) systems. Although the utilization of camera images for HVAC control has been widely studied to optimize thermal comfort, privacy protection of occupant images has not been considered in p...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26184v1
• SecMate: Multi-Agent Adaptive Cybersecurity Troubleshooting with Tri-Context Personalization

  Recent advances in large language models and agentic frameworks have enabled virtual customer assistants (VCAs) for complex support. We present SecMate, a multi-agent VCA for cybersecurity troubleshooting that integrates device, user, and service specificity from conversational and device-level sign...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26394v1
• Enforcing Benign Trajectories: A Behavioral Firewall for Structured-Workflow AI Agents

  Structured-workflow agents driven by large language models execute tool calls against sensitive external environments. We propose \codename, a telemetry-driven behavioral anomaly detection firewall. Drawing on sequence-based intrusion detection, \codename\ compiles verified benign tool-call telemetr...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26274v1
• Cognitive Atrophy and Systemic Collapse in AI-Dependent Software Engineering

  The integration of Large Language Models (LLMs) into the software development lifecycle (SDLC) masks a critical socio-technical failure: Cognitive-Systemic Collapse. This paper introduces "Epistemological Debt," the hidden carrying cost incurred when engineers substitute logical derivation with pass...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26855v1
• Recommendations for Efficient and Responsible LLM Adoption within Industrial Software Development

  Context: Large language models (LLMs) are observed to have a significant positive impact on various software engineering (SE) activities. With improved accessibility, the adoption of powerful LLMs in industry has surged recently. However, there is a lack of actionable best practices for the efficien...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26590v1
• RepoDoc: A Knowledge Graph-Based Framework to Automatic Documentation Generation and Incremental Updates

  Maintaining up-to-date, comprehensive documentation for large codebases is a persistent challenge. Recent progress in automated documentation has moved from template-based rules to large language models (LLMs), yet existing tools still process source code as flat fragments, producing isolated docume...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26523v1
• An Empirical Study of Speculative Decoding on Software Engineering Tasks

  Large Language Models (LLMs) have become widely used for Software Engineering (SE) tasks, spanning from function-level code generation to complex repository-level workflows. However, the high latency of autoregressive inference remains a significant bottleneck, hindering their deployment in interact...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26469v1
• Agentic AI in the Software Development Lifecycle: Architecture, Empirical Evidence, and the Reshaping of Software Engineering

  The arrival of large language models (LLMs) capable of multi-step reasoning, tool use, and long-horizon planning has produced a qualitative shift in software engineering. Where earlier code-completion tools such as GitHub Copilot operated at the granularity of a line or function, modern agentic syst...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26275v1
• LLM-Assisted Empirical Software Engineering: Systematic Literature Review and Research Agenda

  Context: Empirical Software Engineering (ESE) faces increasing challenges due to data scale, methodological complexity, and reproducibility concerns. Large Language Models (LLMs) have emerged as promising tools to support empirical workflows, yet their use remains fragmented, with no comprehensive s...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26192v1
• When Model Editing Meets Service Evolution: A Knowledge-Update Perspective for Service Recommendation

  The rapid evolution of software services poses substantial challenges to the design and implementation of effective recommendation systems. Traditional service recommendation approaches often rely on static representations and historical usage data, which are insufficient for adapting to the dynamic...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26686v1
• Will It Break in Production? Metric-Driven Prediction of Residual Defects in Python Systems

  Python's dynamic nature complicates testing and increases the possibility that some defects evade detection, so an effective fault prediction becomes essential. We examine whether post-release faults can be predicted using modern ML and DL. Using a balanced dataset of over 4,000 labeled faults with ...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26667v1
• Probabilistic Condition, Decision and Path Coverage of Circuit-based Quantum Programs

  Coverage criteria play a central role in assessing test adequacy in classical software, yet their effectiveness for quantum programs remains poorly understood and largely unexplored. In this paper, we propose six quantum-tailored criteria - condition, decision, and path coverage, and their probabili...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26609v1
• Towards Intelligent Computation Offloading in Dynamic Vehicular Networks: A Scalable Multilayer Pipeline

  Software Defined Vehicles face an increasing computational gap as advanced algorithms and frequent software updates demand more processing power while onboard hardware remains static throughout a vehicle's 10+ year lifespan. This mismatch threatens the performance of safety-critical functions includ...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26416v1
• Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech

  Digital biomarkers for depression have largely relied on static acoustic descriptors, pooled summary statistics, or conventional machine learning representations. Such approaches may miss nonlinear temporal organization embedded in conversational vocal dynamics. We hypothesized that depression is as...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26242v1
• Dual-LoRA: Parameter-Efficient Adversarial Disentanglement for Cross-Lingual Speaker Verification

  Cross-lingual speaker verification suffers from severe language-speaker entanglement. This causes systematic degradation in the hardest scenario: correctly accepting utterances from the same speaker across different languages while rejecting those from different speakers sharing the same language. S...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26327v1
• SPG-Codec: Exploring the Role and Boundaries of Semantic Priors in Ultra-Low-Bitrate Neural Speech Coding

  Conventional neural speech codecs suffer from severe intelligibility degradation at ultra-low bitrates, where the bottleneck transitions from acoustic distortion to semantic loss. To address this issue, this paper conducts a systematic investigation into the role and fundamental limits of integratin...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26296v1
• DiffAnon: Diffusion-based Prosody Control for Voice Anonymization

  To preserve or not to preserve prosody is a central question in voice anonymization. Prosody conveys meaning and affect, yet is tightly coupled with speaker identity. Existing methods either discard prosody for privacy or lack a principled mechanism to control the utility-privacy trade-off, operatin...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26281v1
• Factorized Latent Reasoning for LLM-based Recommendation

  Large language models (LLMs) have recently been adopted for recommendation by framing user preference modeling as a language generation problem. However, existing latent reasoning approaches typically represent user intent with a single latent vector, which struggles to capture the inherently multi-...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26760v1
• AgentSim: A Platform for Verifiable Agent-Trace Simulation

  Training trustworthy agentic LLMs requires data that shows the grounded reasoning process, not just the final answer. Existing datasets fall short: question-answering data is outcome-only, chain-of-thought data is not tied to specific documents, and web-agent datasets track interface actions rather ...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26653v1
• Efficient Listwise Reranking with Compressed Document Representations

  Reranking, the process of refining the output from a first-stage retriever, is often considered computationally expensive, especially when using Large Language Models (LLMs). A common approach to mitigate this cost involves utilizing smaller LLMs or controlling input length. Inspired by recent advan...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26483v1
• CARD: Non-Uniform Quantization of Visual Semantic Unit for Generative Recommendation

  Generative recommendation frameworks typically represent items as discrete Semantic IDs (SIDs). While existing studies have sought to enhance SID construction by incorporating multimodal content, collaborative signals, or more advanced quantization techniques, learning high-quality SIDs still faces ...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26427v1
• TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation

  Multimodal recommendation improves user modeling by integrating collaborative signals with heterogeneous item content. In real applications, user interests evolve over time and exhibit nonstationary dynamics, where different preference factors change at different rates. This challenge is amplified i...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26247v1
• ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender Systems

  The remarkable text understanding and generation capabilities of large language models (LLMs) have revitalized the field of general recommendation based on implicit user feedback. Rather than deploying LLMs directly as recommendation models, a more flexible paradigm leverages their ability to interp...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26231v1
• Hierarchical Long-Term Semantic Memory for LinkedIn's Hiring Agent

  Large Language Model (LLM) agents are increasingly used in real-world products, where personalized and context-aware user interactions are essential. A central enabler of such capabilities is the agent's long-term semantic memory system, which extracts implicit and explicit signals from noisy longit...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26197v1
• Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective

  DNNs have gained widespread adoption in feature interaction recommendation models. However, there has been a longstanding debate on their roles. On one hand, some works claim that DNNs possess the ability to implicitly capture high-order feature interactions. Conversely, recent studies have highligh...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26489v1
• Meta-Learning and Targeted Differential Privacy to Improve the Accuracy-Privacy Trade-off in Recommendations

  Balancing differential privacy (DP) with recommendation accuracy is a key challenge in privacy-preserving recommender systems, since DP-noise degrades accuracy. We address this trade-off at both the data and model levels. At the data level, we apply DP only to the most stereotypical user data likely...

  📄 ResearchApr 29, 2026http://arxiv.org/abs/2604.26390v1
• Explainable Prototype Booster: Enhancing Latent Representations of Foundation Models for Gene Expression Prediction

  Spatial transcriptomics (ST) is a cutting-edge technology that measures gene expression while preserving spatial context and generating pathology-grade tissue images. Although ST has enabled numerous discoveries and demonstrated a huge application potential in pathological diagnosis and prognosis, the technology remains time-consuming and costly. The ability to predict gene markers of cancer from histological H&E-stained tissue images can overcome these technological barriers to open new horizons for precision and personalised pathology. Recently, foundation models have demonstrated improvements in generating general-purpose embeddings of H&E-images. However, these improved representations are not optimized for gene expression prediction and lack task-specific adaptability. To address this limitation, we propose the Explainable Prototype Booster (EP-Booster), which incorporates biological prior knowledge to guide the construction and training of learnable prototypes for embedding refin

  📄 ResearchApr 29, 2026https://www.biorxiv.org/content/10.64898/2026.04.27.720478v1?rss=1
• Artificial Intelligence for Cardiac Biomarkers After Myocardial Infarction: A Systematic Review and a Leakage-Aware Modeling Framework

  Aims To systematically evaluate how artificial intelligence and machine-learning (AI/ML) methods are applied to cardiac biomarkers after myocardial infarction (MI), identify recurring methodological limitations, and operationalize a leakage-aware modelling workflow in a proof-of-concept post-MI dataset using a controlled proxy classification task. Methods and results A PRISMA 2020-compliant systematic review of studies published between 2015 and 2025 identified 120 eligible studies from 1,389 records. Most studies used multimodal inputs combining biomarkers with clinical or functional variables (109/120, 90.8%) and focused on prediction or prognostic modelling (89/120, 74.2%). Logistic or regularized regression (76/120, 63.3%) and Random Forest (69/120, 57.5%) were the most frequently used approaches. Internal validation predominated, whereas independent external validation was reported in only 44/120 studies (36.7%). Area under the receiver operating characteristic curve (ROC-AUC) was

  📄 ResearchApr 29, 2026https://www.medrxiv.org/content/10.64898/2026.04.28.26351958v1?rss=1
• Non-Invasive Arterial Blood Pressure Waveform Generation in Critically Ill Patients: A Sensor-Based Deep Learning Approach

  Continuous monitoring of Arterial Blood Pressure (ABP) in critically ill patients requires invasive arterial catheterization, which carries risks of thrombosis, vascular injury and infection. Here, we train and validate a computational model for continuous non-invasive ABP estimation in Intensive Care Unit (ICU) patients using a novel wearable sensor array. The sensor acquires continuous high frequency photoplethysmography (PPG) and electrocardiography (ECG) signals which are used as inputs in a deep learning algorithm for beat-to-beat reconstruction of ABP waveforms. We include 28 patients enrolled in four ICU units at Johns Hopkins Hospital, comprising 15,489 five-second ECG and PPG segments. A CNN/LSTM hybrid architecture achieved an R^2 of 0.812 and a sample-level mean absolute error (MAE) of 4.94 +/- 4.96 mmHg, with systolic and diastolic blood pressure MAEs of 6.38 +/- 6.62 and 3.99 +/- 4.53 mmHg, respectively. This performance closely approached an upper-bound model trained on c

  📄 ResearchApr 29, 2026https://www.medrxiv.org/content/10.64898/2026.04.28.26351954v1?rss=1
• Longitudinal Brain Atrophy Patterns in Dementia and Cognitive Decline: the Framingham Heart Study

  Abstract Background: Characterizing longitudinal patterns of brain atrophy that distinguish Alzheimers disease (AD) and related neurodegeneration along with normative aging remains a major challenge. We aimed to identify data-driven longitudinal brain atrophy components and evaluate their associations with plasma AD biomarkers and cognitive outcomes in a community-based cohort. Methods: We analyzed 756 MRI scans from 300 participants in the Framingham Heart Study (mean 2.52 scans per participant; range 2 to 4). Linear mixed effects models were used to identify MRI features associated with diagnostic group (cognitively normal [CN], mild cognitive impairment [MCI], and dementia). Significant features (n=211) were entered into a longitudinal multivariate decomposition framework (ANOVA Simultaneous Component Analysis with Assorted Linear functions; ALASCA) to derive principal components (PCs) capturing patterns of structural change over time. Associations between PCs and plasma AD biomarke

  📄 ResearchApr 29, 2026https://www.medrxiv.org/content/10.64898/2026.04.28.26351854v1?rss=1
• A Sequential Multiple Assignment Randomized Trial Design with Response-Adaptive Tailoring Function

  We present a novel sequential multiple assignment randomized trial (SMART) design that integrates response-adaptive randomization with tailoring functions (RA-TF-SMART). We develop percentile-based and Z-score RA-TFs that incorporate both within-patient and between-patient adaptation to map continuous outcomes to randomization probabilities. We apply Q-learning, tree-based reinforcement learning, and G-estimation to estimate dynamic treatment regimens (DTRs). We compare our RA-TF-SMART designs to balanced randomized SMARTs (BR-SMARTs), tailoring function SMARTs (TF-SMARTs), and generalized outcome-adaptive SMARTs (GO-SMARTs). This study addresses limitations in SMART methodology by presenting designs where randomization probability does not require dichotomization of continuous outcomes and utilizes both individual patient outcomes and accumulated treatment efficacy data from prior participants. RA-TF-SMARTs offer a flexible framework that maximizes benefit for trial participants while

  📄 ResearchApr 29, 2026https://www.medrxiv.org/content/10.64898/2026.04.28.26351992v1?rss=1
• An Interpretable Deep Learning Framework Reveals Frontoparietal Control Network Hyperactivation Underlying Autism Diagnosis and Symptom Severity

  BACKGROUND: Autism spectrum disorder (ASD) is marked by profound neurobiological heterogeneity, which drives inconsistent neuroimaging findings and impede the discovery of reliable biomarkers for precise diagnosis and phenotypic prediction. Although deep learning has shown promising predictive power, its black-box nature obscures the mechanistic interpretability underlying high-dimensional learned representations, limiting their translation into actionable neurobiological insights. METHODS: We present IBSS-GAT, a novel interpretable deep learning framework that explicitly models the spatiotemporal landscape of individual-specific internal brain states and integrates a two-stage mechanistic interpretability pipeline to bridge model-derived features to well-characterized neurodynamic processes and clinical phenotypes. RESULTS: Across three independent large-scale neuroimaging cohorts, IBSS-GAT achieved state-of-the-art classification performance in both cognitive decoding (99.30% accurac

  📄 ResearchApr 29, 2026https://www.medrxiv.org/content/10.64898/2026.04.28.26351834v1?rss=1
• Rapid Diffusion, Persistent Deserts: A National Longitudinal Study of Geographic Access to Hospitals with AI Deployment, 2022 to 2024

  Measured hospital AI deployment expanded between 2022 and 2024, but did the geography of access converge, and did persistent access deserts overlap with greater health burden? Using two waves of the American Hospital Association (AHA) Annual Survey linked to 2020 Census block-group populations, we estimate contiguous-U.S. coverage for 329.3 million residents and full-frame transition profiles for 334.7 million residents. Measured AI-enabled status is identified using five binary workforce/workflow AI-use items in 2022 and fourteen ordinal clinical and operational AI implementation items in 2024 (primary 2024 threshold: expanding or fully integrated). The share of hospitals reporting active AI deployment rose from 18.3% to 28.6%. Contiguous-U.S. coverage within a 30-minute drive increased from 67.0% to 76.1%, yet spatial inequality grew: the population-weighted Gini coefficient of access distances rose from 0.739 to 0.767. In the full transition frame, 45.1 million people newly crossed

  📄 ResearchApr 29, 2026https://www.medrxiv.org/content/10.64898/2026.04.28.26351957v1?rss=1
• preSCRIPT: Large-scale prescription search and annotation engine for pharmacogenomic studies

  Pharmacogenetics (PGx) has traditionally focused on a small number of high-impact variants affecting drug response due to the fact that PGx studies are labor-intensive and therefore low-throughput. Population biobanks linked to electronic health records (EHRs), including the UK Biobank (UKB) with prescription data for ~230,000 individuals offer opportunities to scale PGx research. This, however, comes with a challenge as EHRs do not provide direct treatment response outcomes. One way to overcome this is to draw indirect drug response phenotypes from prescription records. Here, we propose preSCRIPT, a framework to filter and annotate raw prescriptions from the UKB to derive phenotypes for analyses which includes an algorithm to distinguish short prescription gaps from true dose changes. As a proof of concept, we applied preSCRIPT to warfarin, paracetamol, codeine, amitriptyline, simvastatin, aspirin, and amlodipine and derived therapy length and median daily doses. We tested association

  📄 ResearchApr 29, 2026https://www.medrxiv.org/content/10.64898/2026.04.28.26351989v1?rss=1
• Evaluating Claude’s bioinformatics research capabilities with BioMysteryBench

  Evaluating Claude’s bioinformatics research capabilities with BioMysteryBench

  📄 ResearchApr 29, 2026https://www.anthropic.com/research/Evaluating-Claude-For-Bioinformatics-With-BioMysteryBench
• ‘It’s not okay to steal a charity’: Musk testifies in trial over AI’s future

  ‘It’s not okay to steal a charity’: Musk testifies in trial over AI’s future The Washington Post

  🌐 MovesApr 29, 2026https://www.washingtonpost.com/technology/2026/04/28/elon-musk-testifies-trial-openai/
• Elon Musk vs. OpenAI: What You Need to Know About the Epic $134 Billion Trial

  Elon Musk vs. OpenAI: What You Need to Know About the Epic $134 Billion Trial entrepreneur.com

  🌐 MovesApr 29, 2026https://www.entrepreneur.com/business-news/elon-musk-vs-openai-what-you-need-to-know-about-the-epic-134-billion-trial