Three major AI trends are converging: rapid model improvements in the US and China, the US government moving toward stricter AI regulation, and both nations considering blocking access to their best A…
Congressional lawmakers must be prepared to discuss key AI policy questions, including regulation, ethics, and national security implications, as artificial intelligence rapidly advances. The article …
Hong Kong is shifting its financial focus toward the Middle East, while China faces challenges with AI persona technologies. The developments highlight evolving tech and economic dynamics in Asia.…
Singapore's sovereign wealth fund Temasek plans to increase its AI investment exposure from 6% to 15% of its $400 billion portfolio by 2031, targeting areas including energy, data centers, semiconduct…
AI-powered financial advisers are gaining an edge over traditional human rivals by leveraging data analysis and personalized recommendations, according to a Financial Times report. The technology enab…
Researchers developed a physics-guided machine learning framework that integrates domain knowledge into deep learning models to predict fuel density for wildfire management, outperforming purely data-…
Researchers found substantial cross-lingual disparities in healthcare LLM Q&A, with models aligning more with English Wikipedia even for non-English prompts. Providing non-English contextual excerpts …
Researchers developed a multimodal generative framework for structural and functional MRI features, evaluating VAEs, transformers, GANs, and diffusion models. Their proposed graph VAE (gMMVAE) outperf…
Researchers introduced Gimitest, an open-source framework for testing reinforcement learning policies across diverse environments and algorithms. The tool supports single- and multi-agent RL policies,…
A new study from arXiv shows that grounding LLM-generated tests in a specification improves code correctness by 38 percentage points over baseline across multiple Claude models, while merely increasin…
A study on text-to-SQL systems found that verification-based signals, such as LLM judges, outperform black-box signals like self-consistency in predicting query correctness, with a two-provider ensemb…
Researchers at arXiv show that biased LLM judges silently disable skill retirement in self-evolving agents, causing a mechanism failure that no amount of data can fix. The study reveals that false-pas…
Researchers introduced GhostWriter, a novel attack that poisons the long-term memory of personal AI agents by injecting hidden payloads, achieving 98% injection and 60% activation rates against state-…
Researchers developed a non-contact, real-time heart rate measurement system using image processing with commodity cameras and AI agents. The system uses face detection and deep learning to compute he…
A new study from arXiv finds that LLM-generated skill files do not improve the performance of AI data scientists across four data-science lifecycle stages. In 7,560 runs covering 56 tasks, nine model …
A comprehensive survey of 1,250 arXiv papers (2024-2026) on AI self-improvement reveals a taxonomy distinguishing bounded self-refinement from open-ended recursive self-improvement (RSI), with the lat…
Researchers introduced Pyligent, a training framework that teaches AI models to recover from errors by learning from failed reasoning branches. In tests on hidden graphs, Sudoku, and Blocksworld, Pyli…
Researchers introduced institutional red-teaming, a methodology for testing deployment rules in multi-agent AI systems, and found that changing only the consequence rule shifts mean fatality by 22 to …
Researchers propose a new evaluation methodology for LLM-powered software engineering agents, focusing on contamination-awareness, in-the-wild behavior assessment, and trajectory-aware benchmarks to b…
Researchers propose PRoVeFL, a federated learning framework that combines privacy preservation, Byzantine robustness, and verifiable aggregation using multi-key fully homomorphic encryption across mul…