AI News Archive: May 7, 2026 — Part 17
Sourced from 500+ daily AI sources, scored by relevance.
- Greece, birthplace of democracy, seeks to put humanity ahead of AI in updated constitution
Greece is preparing sweeping constitutional reforms that would require artificial intelligence to serve human society and protect individual freedoms.
- Tech is turning increasingly to religion in a quest to create ethical AI
Tech companies are increasingly seeking guidance from faith leaders to shape artificial intelligence.
- Spotify’s AI-powered personal DJ adds support for more countries and languages
Spotify announced today that Premium users in more than 75 countries can now interact with the platform’s AI-powered DJ, in an expansion that also includes support for four new languages. Here are the details. more…
- OpenClaw and Claude can put your AI-generated podcasts in Spotify
A new command-line tool lets AI agents save audio alongside your other podcasts.
- Google Shuts Down 'Project Mariner' Web Browsing AI
Google Shuts Down 'Project Mariner' Web Browsing AI PCMag
- Google Shuts Down 'Project Mariner' Web Browsing AI
Google Shuts Down 'Project Mariner' Web Browsing AI PCMag Australia
- Google Shuts Down 'Project Mariner' Web Browsing AI
Google Shuts Down 'Project Mariner' Web Browsing AI PCMag Middle East
- Microsoft VP Backs Removing Copilot From Less Useful Corners of Windows
Microsoft VP Backs Removing Copilot From Less Useful Corners of Windows PCMag Australia
- Microsoft VP Backs Removing Copilot From Less Useful Corners of Windows
Microsoft VP Backs Removing Copilot From Less Useful Corners of Windows PCMag UK
- White House calls Star Wars actor ‘sick’ for AI photo of Trump
White House calls Star Wars actor ‘sick’ for AI photo of Trump
- VIDEO: Oman wealth fund invests in Elon Musk’ brain chip firm Neuralink
Oman Investment Authority (OIA), the sovereignwealth fund of the sultanate, has invested in. Elon Musk’s brain chip firm Neuralink
- A Chatbot Claimed to Be a Licensed Psychiatrist. Now Pennsylvania Is Suing the Company That Makes It.
A Chatbot Claimed to Be a Licensed Psychiatrist. Now Pennsylvania Is Suing the Company That Makes It. entrepreneur.com
- Pentagon will ‘never again’ rely on a single AI provider, official says
Defense Under Secretary for Research and Engineering Emil Michael said new agreements with Big Tech companies are a “counterstatement” to the ongoing Anthropic-Pentagon conflict as the agency prioritizes flexible contracts.
- Testkube Redefines Testing for the AI Era with Testkube AI, while Deepening its Commitment to Open Source and Enterprise Teams
Testkube Redefines Testing for the AI Era with Testkube AI, while Deepening its Commitment to Open Source and Enterprise Teams USA Today
- Inspired by the brain, researchers build smarter and more efficient computer hardware
As traditional computer chips reach their physical limits and artificial intelligence demands more energy than ever, University of Missouri researchers are rethinking how computers work by taking cues from the human brain. The timing is critical. Energy use from AI data centers is projected to double by the end of the decade, raising urgent questions about sustainability.
- The appointment recognises Asst Prof Mengaldo's expertise at the intersection of AI, weather and climate science.
The appointment recognises Asst Prof Mengaldo's expertise at the intersection of AI, weather and climate science. EurekAlert!
- Former OpenAI Researcher To Raise $500 Million For AI Science Startup
Periodic Labs, which plans to push breakthroughs in physics and chemistry using artificial intelligence, has seen its valuation jump sixfold in just eight months.
- Google’s taking a big swing at AI health with the Fitbit Air
Google kicks off a new era with its first Fitbit tracker in four years, an app rebrand, and its AI coach leaving beta.
- AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents
Recent LLM-based agents have closed substantial portions of the scientific discovery loop in software-only machine-learning research, in chemistry, and in biology. Extending the same loop to high-fidelity physical simulators is harder, because solver completion does not imply physical validity and m...
- UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
Self-distillation (SD) offers a promising path for adapting large language models (LLMs) without relying on stronger external teachers. However, SD in autoregressive LLMs remains challenging because self-generated trajectories are free-form, correctness is task-dependent, and plausible rationales ca...
- Cross-Modal Navigation with Multi-Agent Reinforcement Learning
Robust embodied navigation relies on complementary sensory cues. However, high-quality and well-aligned multi-modal data is often difficult to obtain in practice. Training a monolithic model is also challenging as rich multi-modal inputs induce complex representations and substantially enlarge the p...
- DINORANKCLIP: DINOv3 Distillation and Injection for Vision-Language Pretraining with High-Order Ranking Consistency
Contrastive language-image pretraining (CLIP) suffers from two structural weaknesses: the symmetric InfoNCE loss discards the relative ordering among unmatched in-batch pairs, and global pooling collapses the visual representation into a semantic bottleneck that is poorly sensitive to fine-grained l...
- Towards Metric-Faithful Neural Graph Matching
Graph Edit Distance (GED) is a fundamental, albeit NP-hard, metric for structural graph similarity. Recent neural graph matching architectures approximate GED by first encoding graphs with a Graph Neural Network (GNN) and then applying either a graph-level regression head or a matching-based alignme...
- NeuroAgent: LLM Agents for Multimodal Neuroimaging Analysis and Research
Multimodal neuroimaging analysis often involves complex, modality-specific preprocessing workflows that require careful configuration, quality control, and coordination across heterogeneous toolchains. Beyond preprocessing, downstream statistical analysis and disease classification commonly require ...
- Improved techniques for fine-tuning flow models via adjoint matching: a deterministic control pipeline
We propose a deterministic adjoint matching framework that formulates human preference alignment for flow-based generative models as an optimal control problem over velocity fields. One can directly regress the control toward a value-gradient-induced target under the current policy, leading to a sim...
- Directional Consistency as a Complementary Optimization Signal: The GONO Framework
We identify and formalize an underexplored phenomenon in deep learning optimization: directional alignment and loss convergence can be decoupled. An optimizer can exhibit near-perfect directional consistency (cc_t -> 1, measured via consecutive gradient cosine similarity) while the loss remains high...
- Coordination Matters: Evaluation of Cooperative Multi-Agent Reinforcement Learning
Cooperative multi-agent reinforcement learning (MARL) benchmarks commonly emphasize aggregate outcomes such as return, success rate, or completion time. While essential, these metrics often fail to reveal how agents coordinate, particularly in settings where agents, tasks, and joint assignment choic...
- Continuous Latent Diffusion Language Model
Large language models have achieved remarkable success under the autoregressive paradigm, yet high-quality text generation need not be tied to a fixed left-to-right order. Existing alternatives still struggle to jointly achieve generation efficiency, scalable representation learning, and effective g...
- On the Implicit Reward Overfitting and the Low-rank Dynamics in RLVR
Recent extensive research has demonstrated that the enhanced reasoning capabilities acquired by models through Reinforcement Learning with Verifiable Rewards (RLVR) are primarily concentrated within the rank-1 components. Predicated on this observation, we employed Periodic Rank-1 Substitution and i...
- Learning to Cut: Reinforcement Learning for Benders Decomposition
Benders decomposition (BD) is a widely used solution approach for solving two-stage stochastic programs arising in real-world decision-making under uncertainty. However, it often suffers from slow convergence as the master problem grows with an increasing number of cuts. In this paper, we propose Re...
- Is One Layer Enough? Understanding Inference Dynamics in Tabular Foundation Models
Transformer-based tabular foundation models (TFMs) dominate small to medium tabular predictive benchmark tasks, yet their inference mechanisms remain largely unexplored. We present the first large-scale mechanistic study of layerwise dynamics in 6 state-of-the-art tabular in-context learning models....
- PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization
We introduce PACZero, a family of PAC-private zeroth-order mechanisms for fine-tuning large language models that delivers usable utility at $I(S^*; Y_{1:T})=0$. This privacy regime bounds the membership-inference attack (MIA) posterior success rate at the prior, an MIA-resistance level the DP framew...
- Memory Efficient Full-gradient Attacks (MEFA) Framework for Adversarial Defense Evaluations
This work studies the robust evaluation of iterative stochastic purification defenses under white-box adversarial attacks. Our key technical insight is that gradient checkpointing makes exact end-to-end gradient computation through long purification trajectories practical by trading additional recom...
- SwiftI2V: Efficient High-Resolution Image-to-Video Generation via Conditional Segment-wise Generation
High-resolution image-to-video (I2V) generation aims to synthesize realistic temporal dynamics while preserving fine-grained appearance details of the input image. At 2K resolution, it becomes extremely challenging, and existing solutions suffer from various weaknesses: 1) end-to-end models are ofte...
- NavOne: One-Step Global Planning for Vision-Language Navigation on Top-Down Maps
Existing Vision-Language Navigation (VLN) methods typically adopt an egocentric, step-by-step paradigm, which struggles with error accumulation and limits efficiency. While recent approaches attempt to leverage pre-built environment maps, they often rely on incrementally updating memory graphs or sc...
- Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement
Training world models on vast quantities of unlabelled videos is a critical step toward fully autonomous intelligence. However, the prevailing paradigm of encoding raw pixels into opaque latent spaces and relying on heavy decoders for reconstruction leaves these models computationally expensive and ...
- Eulerian Motion Guidance: Robust Image Animation via Bidirectional Geometric Consistency
Recent advancements in image animation have utilized diffusion models to breathe life into static images. However, existing controllable frameworks typically rely on Lagrangian motion guidance, where optical flow is estimated relative to the initial frame. This paper revisits the same optical-flow p...
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- Verifier-Backed Hard Problem Generation for Mathematical Reasoning
Large Language Models (LLMs) demonstrate strong capabilities for solving scientific and mathematical problems, yet they struggle to produce valid, challenging, and novel problems - an essential component for advancing LLM training and enabling autonomous scientific research. Existing problem generat...
- Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less
Optimizers play an important role in both pretraining and finetuning stages when training large language models (LLMs). In this paper, we present an observation that full finetuning with the same optimizer as in pretraining achieves a better learning-forgetting tradeoff, i.e., forgetting less while ...
- When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels
Many deployments must compare candidate language models for safety before a labeled benchmark exists for the relevant language, sector, or regulatory regime. We formalize this setting as benchmarkless comparative safety scoring and specify the contract under which a scenario-based audit can be inter...
- Are We Making Progress in Multimodal Domain Generalization? A Comprehensive Benchmark Study
Despite the growing popularity of Multimodal Domain Generalization (MMDG) for enhancing model robustness, it remains unclear whether reported performance gains reflect genuine algorithmic progress or are artifacts of inconsistent evaluation protocols. Current research is fragmented, with studies var...
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