All 3,125 AI stories, newest first · page 61 of 131
A new study reveals that large language models (LLMs) overwhelmingly recommend well-known brands, creating a 'Conditional Monopoly' that could stifle competition. Researchers tested three major AI models with skincare products and found a clear bias toward established names, even when smaller brands might be just as good.
Researchers used AI coding agents to autonomously direct robots through complex tasks like installing GPUs and cutting zip-ties. This breakthrough could reduce the need for manual programming in manufacturing and other industries.
Researchers introduced a self-evolving AI agent that improves legal case searches by rewriting queries to boost BM25 performance without any parameter training. This could streamline legal research for professionals.
An AI agent is now hiring humans to handle tasks like shipping user-generated content (UGC) videos, paying them at rates competitive with AI services. This blurs the line between human and AI labor in the gig economy.
Researchers have proposed a new way to define good AI explanations, focusing on counterfactuals and user beliefs. This could help AI systems explain their decisions more clearly to everyday users.
Researchers have developed a new method called Telegraph English that compresses information for AI models while preserving meaning. This could make AI answers more accurate and efficient, especially for complex, multi-step questions.
Researchers have identified memory traces in AI models that behave similarly to biological memory units in human brains. This discovery could help us understand how AI learns and remembers, potentially improving future AI systems.
Scientists have created a method to measure how much AI agents trust each other, using a survival game where verification is costly. This could help design better teams of AI agents for complex tasks.
Researchers introduced PrologMCP, an open-source server that helps AI models solve complex problems by delegating deductive reasoning to Prolog. This could make AI systems more reliable for tasks requiring deep logical analysis.
A new research benchmark called PhoneHarness tests how well AI agents can handle real mobile tasks—combining app interfaces, device commands, and external tools. This marks a shift from simply predicting screen taps to completing entire workflows.
Researchers introduced OSGuard, a new benchmark to test if AI agents complete tasks safely. It checks for risky shortcuts that might bypass security or ethics rules.
OpenAI has developed Deployment Simulation, a method to predict how AI models will behave in real-world scenarios before they're released. This could make AI systems safer and more reliable for everyday users.
Researchers found that AI memory and skill modules don't always justify their cost. The study suggests that simpler approaches — such as using the same token budget for additional actor steps — can be just as effective or better for certain web-based tasks.
A new study reveals that current AI models favor languages like English and French, making them less effective and more expensive for speakers of underrepresented languages. Researchers propose new methods to make these models more fair and efficient for everyone.
Researchers created a new test to see how well AI models handle messy, real-world time data. This could improve AI tools that analyze everything from medical sensors to industrial equipment.
Researchers introduced ReportQA, a new AI system that evaluates radiology reports by asking and answering clinically relevant questions. Unlike traditional metrics, ReportQA mimics how doctors use reports for diagnosis, potentially improving the quality and usefulness of AI-generated medical reports.
Researchers found that AI models often waste time overthinking, leading to errors. Their new method stops the AI when it's no longer helping, improving accuracy.
Researchers found that AI models often sound too confident in answers that aren't fully justified. They developed a new method to better align confidence with the quality of explanations. This could make AI assistants more reliable when giving complex answers.
Researchers developed Metric Match, a subset selection method that accurately estimates LLM judge reliability from limited human annotations, potentially reducing the cost of AI evaluation.
Researchers developed an AI system that translates natural language queries into requests for satellite imagery and environmental data. This could make complex geospatial data more accessible to non-experts.
Researchers developed a new AI framework called SERAF that enhances time series forecasting by combining historical patterns with semantic context to address non-stationarity. This approach could make predictions more accurate for real-world applications like stock markets and weather forecasting.
Researchers introduced Nemotron 3 Ultra, a massive AI model with 550 billion total parameters and 55 billion active parameters. It uses advanced techniques like a hybrid Mamba-Transformer architecture, Mixture-of-Experts, and Multi Token Prediction to handle long texts and complex reasoning tasks more efficiently than ever before.
A critical flaw in Microsoft Copilot allowed hackers to steal two-factor authentication codes. This highlights ongoing security risks in AI systems.
Researchers have developed new AI models that respond instantly while maintaining strong reasoning. These models could make AI tools faster and more capable for everyday users. The models, Ling-2.6 and Ring-2.6, are designed to be efficient and practical to use, potentially improving AI assistants and other tools we interact with daily.