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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.
Anthropic shut down its newest AI models after a US government request, blocking access for foreign nationals. This incident underscores the risks of relying on American-controlled AI technology.
Researchers tested AI tools that turn human-written math proofs into computer code. They found these tools work well with clean, ideal proofs but fail with real-world, messy ones. This highlights a big gap in AI's ability to robustly handle informal mathematics.
Researchers developed a new type of AI model that can reason about unseen combinations of objects by combining causal and relational reasoning. This breakthrough could help AI systems generalize better to new situations.
Researchers introduced AdaMame, a new AI training approach that helps large language models reason better in multiple languages. This could make math-solving AI tools more reliable for non-English speakers.
Researchers introduced YeasierAgent, a system that lets users and AI agents collaborate to build apps. It rethinks how software is created, making it more flexible and social.
Researchers have developed TwinBI, an AI system that bridges the gap between interactive dashboards and natural language queries. By creating a digital twin of the dashboard state, TwinBI ensures filters, metrics, and chart contexts stay synchronized whether you're clicking or typing.
Transpilatron is an AI tool that converts Python code into C binaries, making it easier to run Python programs on devices with limited resources. This could be a game-changer for developers working with embedded systems or older hardware.
Salesforce acquired Fin, an AI customer service platform, to enhance its Agentforce tool. This could make it easier for businesses to build custom AI assistants for tasks like handling customer inquiries.