AI News Archive: May 4, 2026 — Part 18
Sourced from 500+ daily AI sources, scored by relevance.
- Distributed Observer-based Fault Detection over Intelligent Networked Multi-Vehicle Systems
Decentralized strategies are of interest for local decision-making over multi-vehicle networks. This paper studies mixed traffic networks of human-driven and autonomous vehicles with partial sensor measurements. The idea is to enable the group of connected autonomous vehicles (CAVs) to track the sta...
- Dramaturgies of Deception: AI Humanizers and the Performance of Legitimacy in Higher Education Assessment
Artificial intelligence (AI) has disrupted assessment in higher education and accelerated a cycle of compounding performances. Institutional policies demand the demonstration of independent authorship, while commercial AI-enabled services allow students to simulate independent thought and writing. T...
- From 'Here' to 'There': Exploring Proximity Semantics in Multimodal Data Exploration
Modern data exploration tools often struggle to capture the subtleties of analytical intent, especially when users seek patterns that are difficult to specify using traditional query methods or natural language alone. We introduce a multimodal research probe for querying time-series and geospatial d...
- Exploring Instant Photography using Generative AI: A Design Probe with the UnReality Camera
Generative AI has increasingly been used for artistic creation, but little work has explored how it shapes the experiential meaning of practice. We consider how generative AI might transform the embodied and tangible process of instant photography through the UnReality Camera, an AI-mediated instant...
- Augmenting Interface Usability Heuristics for Reliable Computer-Use Agents
Recent advances have enabled general computer-use agents that interpret screens and execute grounded actions from human instructions, yet they still struggle to generalize to unseen and evolving interfaces. While improving agent capability remains important, agent compatible interface design offers ...
- The 2026 ACII Dyadic Conversations (DaiKon) Workshop & Challenge
The 2026 ACII Dyadic Conversations (ACII-DaiKon) Workshop & Challenge introduces a benchmark for modeling interpersonal affect and social dynamics in dyadic conversations. Although conversational affect modeling has advanced rapidly, most benchmarks remain speaker-centric and underrepresent coupled,...
- A Low-Code Approach for the Automatic Personalization of Conversational Agents
In this paper, we conducted an SLR on the state of user modeling in the MDE domain. Results show a diverse set of disconnected proposals, covering a partial number of dimensions with an emphasis on those characteristics that are easier to profile. Moreover, most dimensions are regarded as fixed inst...
- Autonomous LLM Agent Worms: Cross-Platform Propagation, Automated Discovery and Temporal Re-Entry Defense
Autonomous LLM agents operate as long-running processes with persistent workspaces, memory files, scheduled task state, and messaging integrations. These features create a new propagation risk: attacker-influenced content can be written into persistent agent state, re-enter the LLM decision context ...
- AI chipmaker Cerebras is kicking off its IPO roadshow
The Nvidia rival is seeking to raise as much as $3.5 billion and achieve a valuation of up to $26.6 billion on the Nasdaq
- GuardSec: A Multi-Modal Web Platform for Real-Time Digital Fraud Detection, Entity Verification, and Connection Security Analysis in the African Context
Online fraud in Africa has reached epidemic scale, yet the few cybersecurity tools that exist are not available to ordinary citizens and are calibrated almost exclusively for SOCs and technically literate users operating on stable broadband connections. This mismatch is not incidental: it is the pre...
- Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning
The growing development of artificial intelligence based solutions, together with privacy legislation, has driven the rise of the so-called privacy preserving machine learning architectures, such as federated learning. While federated learning enables model training on decentralized data preventing ...
- On the Privacy of LLMs: An Ablation Study
Large language models (LLMs) are increasingly deployed in interactive and retrieval-augmented settings, raising significant privacy concerns. While attacks such as Membership Inference (MIA), Attribute Inference (AIA), Data Extraction (DEA), and Backdoor Attacks (BA) have been studied, they are typi...
- When Alignment Isn't Enough: Response-Path Attacks on LLM Agents
Bring-Your-Own-Key (BYOK) agent architectures let users route LLM traffic through third-party relays, creating a critical integrity gap: a malicious relay can modify an aligned LLM response after generation but before agent execution. We formalize this post-alignment tampering threat and show that, ...
- Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery
Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients upload manipulated updates to degrade the performance of the global model. Although detection methods can identify and remove malicious clients, the model remains affected. Retraining from scratch is effective but co...
- Detecting Adversarial Data via Provable Adversarial Noise Amplification
The nonuniform and growing impact of adversarial noise across the layers of deep neural networks has been used in the literature, without a formal mathematical justification, to detect adversarial inputs and improve robustness. In this work, we study this phenomenon in detail and present a formal ad...
- VertMark: A Unified Training-Free Robust Watermarking Framework for Vertical Domain Pre-trained Language Models
With the application of vertical domain pre-trained language models (VPLMs) in specialized fields such as medical, finance, and law, model parameters and inference capabilities have become important digital assets. Achieving traceable copyright verification for VPLMs has become an urgent challenge. ...
- LLM-Assisted Repository-Level Generation with Structured Spec-Driven Engineering
State-of-the-art Large Language Models (LLMs) excel in code generation at the function level. However, the output quality significantly declines when scaling to repository-level systems. Current workflows relying only on natural language prompts suffer from inherent ambiguity and a lack of verifiabi...
- Causal Software Engineering: A Vision and Roadmap
Software engineering increasingly involves making high-stakes decisions under uncertainty, using signals from code, field data, and socio-technical processes. Recent AI-driven support (e.g., anomaly detection, predictive analytics, AIOps, as well as LLM-based agents) has amplified engineers' ability...
- ARIADNE: Agentic Reward-Informed Adaptive Decision Exploration via Blackboard-Driven MCTS for Competitive Program Generation
Competitive program generation aims to automatically produce correct and efficient solutions for programming-contest problems under strict time and memory constraints. Existing LLM-based approaches often fail to perform explicit algorithmic planning and to handle edge cases robustly, leading to unre...
- AOCI: Symbolic-Semantic Indexing for Practical Repository-Scale Code Understanding with LLMs
Large language models struggle with understanding codebases beyond a certain scale -- repositories with hundreds of thousands of lines of code. Existing methods -- retrieval, summarization, agent exploration -- each construct a different view at query time. The view varies between runs, and what per...
- HEJ-Robust: A Robustness Benchmark for LLM-Based Automated Program Repair
Recent Large Language Models (LLMs) have shown strong performance on automated program repair across standard benchmarks. However, these benchmarks evaluate models on a single canonical form of buggy code and do not reflect the syntactic variations commonly observed in real-world software, leaving r...
- Beyond Translation Accuracy: Addressing False Failures in LLM-Based Code Translation
Large Language Models (LLMs) have achieved remarkable success in automated code translation. While prior work has focused on improving translation accuracy through advanced prompting and iterative repair, the reliability of the underlying evaluation frameworks has received less attention. In this pa...
- EvoPoC: Automated Exploit Synthesis for DeFi Smart Contracts via Hierarchical Knowledge Graphs
Smart contract vulnerabilities in Decentralized Finance caused over billions of dollars losses every year, yet the security community faces a critical bottleneck: identifying a vulnerability is not the same as proving it is exploitable. Manual PoC construction is prohibitively labor-intensive, leavi...
- These Aren't the Reviews You're Looking For How Humans Review AI-Generated Pull Requests
We analyze code review interactions for AI-generated pull requests (PRs) on GitHub using the AIDev dataset and compare them to human-authored PRs within the same repositories. We find that most AI-generated PRs receive no review and, when reviewed, are largely dominated by AI agents rather than huma...
- The Conversations Beneath the Code: Triadic Data for Long-Horizon Software Engineering Agents
Frontier software engineering agents have saturated short-horizon benchmarks while regressing on the work that constitutes senior engineering: long-horizon, multi-engineer, ambiguous-specification deliverables. This paper takes a position on what training data is needed to close the gap. The substra...
- DocSync: Agentic Documentation Maintenance via Critic-Guided Reflexion
Software documentation frequently drifts from executable logic as codebases evolve, creating technical debt that degrades maintainability and causes downstream API misuse. While static analysis tools can detect the absence of documentation, they cannot evaluate its semantic consistency. Conversely, ...
- Multi-Axis Speech Similarity via Factor-Partitioned Embeddings
Speech encodes multiple simultaneous attributes--linguistic content, speaker identity, dialect, gender--that conventional single-vector embeddings conflate. We present a factor-partitioned embedding framework that maps each utterance into a single vector whose subspaces correspond to distinct axes...
- Neck-Learn: Attention-Based Multiple Instance Learning and Ensemble Framework for Ecological Momentary Assessment
Vocal hyperfunction (VH) is a prevalent voice disorder whose ambulatory detection remains challenging despite extensive daily voice data. Prior approaches capture week-long neck-surface accelerometer recordings but collapse them into fixed-length subject-level feature vectors, discarding within-day ...
- Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems
LLM agents are increasingly used for personalization due to their ability to communicate directly with users in natural language, integrate external knowledge bases, and negotiate with other (possibly human) agents. Especially in multistakeholder AI systems with multiple distinct objectives, LLM age...
- From Experimental Limits to Physical Insight: A Retrieval-Augmented Multi-Agent Framework for Interpreting Searches Beyond the Standard Model
Modern searches for physics beyond the Standard Model produce rapidly expanding literature containing heterogeneous information, including textual analyses, numerical datasets, and graphical exclusion limits. Integrating these distributed sources remains a time-consuming and manual process for physi...
- Bridging Behavior and Semantics for Time-aware Cross-Domain Sequential Recommendation
Cross-domain sequential recommendation (CDSR) alleviates interaction sparsity by jointly modeling user behaviors across multiple domains. While current studies have made some progresses, they still neglect two issues that severely impact recommendation performance: (i) ignoring domain-specific inter...
- LNGCN: A Distance-Aware Dynamics Network for Protein-Protein Interaction Prediction
High-throughput accurate protein-protein interaction (PPI) prediction is foundational to systems-level biological understanding, disease mechanism dissection, and structure-based drug discovery. Traditional graph convolutional networks (GCNs) are limited by discrete information propagation, layer-wise representation homogenization, and absent continuous-time state evolution, failing to capture residues' 3D spatial hierarchical dynamic binding patterns. We present LNGCN, a hybrid framework integrating liquid neural networks with GCNs, which encodes residue radial distances as node-level driving terms for continuous updates with hierarchical probabilistic calibration. On standard benchmarks, LNGCN achieves $90%$ relative AUPRC improvement over PIPR, outperforms RF2-PPI on $1:10$ imbalanced datasets, and retains $0.9324$ AUPRC on held-out yeast test data. LNGCN further demonstrates biological utility in phosphorylation-dependent SHP2 signaling, FGF23-FGFR1c-$alpha$-Klotho ternary assembly
- Engaging Community and Healthcare Stakeholders in the Design of HIV Retesting Messages: Findings from Human-Centered Design Workshops in Kenya and Uganda
Frequent HIV testing, or "retesting," the practice of regular HIV testing following a negative test result, among persons at high risk of HIV exposure is critical for initiating treatment early among newly infected persons and reducing the risk of HIV transmission. However, barriers to HIV retesting, such as fear of stigma, underestimating risk after a prior negative HIV test, and navigating the logistics of accessing an HIV test, have contributed to lower-than-desired retesting rates in Sub-Saharan Africa, where median time from infection to diagnosis is over 2.5 years. The Innovative Behavioral Intervention Strategies (IBIS) study aims to encourage re-testing by utilizing principles of behavioral economics and human-centered-design in a many-arm randomized trial (known as a "megatrial") of avatar-delivered video-based messages and text messages to promote HIV retesting. In 2025, we conducted two-day focus groups in Kenya and Uganda to prototype the messages among community members an
- AI-based discovery of functional boundaries in the human brain from intraoperative electrophysiology
Neurosurgical and neuromodulation therapies such as deep brain stimulation (DBS) require millimeter-level accuracy to effectively target functional brain regions. Yet, many neuroanatomical boundaries remain invisible to current imaging and electrophysiology methods, limiting precision and contributing to suboptimal patient outcomes. Here, we introduce a self supervised artificial intelligence (AI) framework that learns to delineate functional subregions directly from the spectral content of intraoperative local field potential (LFP) recordings, without the need for predefined biomarkers or anatomical labels. The framework identifies physiologic structure across the full spectrum of the signal and, through explainable AI (XAI), reveals the specific frequency components underlying these distinctions. Validated in the subthalamic nucleus (STN), the model aligned with clinically defined borders and rediscovered known beta oscillations. Applied to the motor thalamus in tremor patients, it c
- Cerebras targets $40 billion valuation in second IPO attempt
AI chip maker Cerebras Systems is heading to the Nasdaq under the ticker CBRS. The IPO roadshow kicks off Monday, with shares targeted between $115 and $125, Reuters reports, citing a person familiar with the matter. The article Cerebras targets $40 billion valuation in second IPO attempt appeared first on The Decoder .
- Manifold
Pick what emails and files Claude and Codex see.
- Rank Monster
See why AI search ignores you — and fix it
- Blackstone and Goldman among backers for $1.5bn JV with Anthropic
New consulting company to advise Wall Street groups on how to deploy its AI across their investment portfolios
- Claude Is Coming to Your Office: Anthropic Announces $1.5 Billion Venture to Rewire Businesses
The AI company recently formed a new joint firm with Blackstone, Goldman Sachs, and Hellman & Friedman centered around the distribution of AI tools.
- New AI model reads DNA sequences to reconstruct ancestry
New AI model reads DNA sequences to reconstruct ancestry EurekAlert!
- AI fails to make inroads with cybercriminals, study finds
AI fails to make inroads with cybercriminals, study finds EurekAlert!
- No digital content is safe from generative AI, researchers say
No digital content is safe from generative AI, researchers say EurekAlert!
- Mythos AI is a cybersecurity threat, but it doesn't rewrite the rules of the game
The cybersecurity community went on alert when Anthropic announced on April 7, 2026, that its latest and most capable general-purpose large language model, Claude Mythos Preview, had demonstrated remarkable—and unintended—capabilities. The artificial intelligence system was able to find and exploit software vulnerabilities—the most serious type of software bugs—at a rate not seen before.
- This Microsoft exec is leading an urgent push to get schools ready for AI
The five year, $4 billion program is aimed at helping schools, nonprofits and young people prepare for the impending disruption AI will bring.
- Walmart leak shows an expanding Onn lineup with a Gemini-ready smart speaker
A certification listing points to Walmart’s Onn lineup expanding with a Gemini-powered smart speaker featuring Matter support, Google Cast, and a 10W speaker.
- Tesla just hit a ‘symbolic’ self-driving milestone. Real-world success will be tougher.
Tesla has reached the threshold Musk once said would be required to achieve “safe unsupervised self-driving,” but the company still faces various hurdles.
- Palantir Earnings: Will AI Commercial Revenue Drive Up Software Stocks?
Palantir earnings are due Monday night. Will the results buoy PLTR and software stocks? Palantir stock has retreated 19% in 2026. The post Palantir Earnings: Will AI Commercial Revenue Drive Up Software Stocks? appeared first on Investor's Business Daily .
- Colorado AI law revisions change key elements of state's controversial rules
Business and labor figures contacted by the Denver Business Journal agreed that SB 189 was imperfect but provided a start to the process of amending the state's pioneering AI law.
- Stevens researchers develop a novel approach to training ai that saves energy, improves speed and minimizes amount of data sent across networks
Stevens researchers develop a novel approach to training ai that saves energy, improves speed and minimizes amount of data sent across networks EurekAlert!
- Chinese Worker Sued After His Company Replaced Him With AI. He Just Won
Chinese Worker Sued After His Company Replaced Him With AI. He Just Won PCMag Australia