Clinical coding is a critical task in healthcare, although traditional methods for automating clinical coding may not provide sufficient explicit evidence for coders in production environments. This evidence is crucial, as medical coders have to make sure there exists at least one explicit passage in the input health record that justifies the attribution of a code. We therefore propose to reframe the task as an entity linking problem, in which each document is annotated with its set of codes and respective textual evidence, enabling better human-machine collaboration. By leveraging parameter-efficient fine-tuning of Large Language Models (LLMs), together with constrained decoding, we introduce three approaches to solve this problem that prove effective at disambiguating clinical mentions and that perform well in few-shot scenarios.

This article surveys Evaluation models to automatically detect hallucinations in Retrieval-Augmented Generation (RAG), and presents a comprehensive benchmark of their performance across six RAG applications. Methods included in our study include: LLM-as-a-Judge, Prometheus, Lynx, the Hughes Hallucination Evaluation Model (HHEM), and the Trustworthy Language Model (TLM). These approaches are all reference-free, requiring no ground-truth answers/labels to catch incorrect LLM responses. Our study reveals that, across diverse RAG applications, some of these approaches consistently detect incorrect RAG responses with high precision/recall.

You've seen headlines about the market crash and maybe even wondered if now's your shot at finally investing. A stock-picking tool powered by OpenAI is helping regular folks identify strong opportunities with minimal risk. Meet Sterling Stock Picker, the thing that could turn your savings account into an early retirement, extra travel funds, or whatever you wish. Rather than gambling with your hard-earned dollars, this tool helps you research options that match your preferences and risk tolerance, and a lifetime subscription is just 68.99 (reg. Want to dive into the stock market but feel like you're reading a foreign language?

The Model Context Protocol (MCP) is a standardized interface designed to enable seamless interaction between AI models and external tools and resources, breaking down data silos and facilitating interoperability across diverse systems. This paper provides a comprehensive overview of MCP, focusing on its core components, workflow, and the lifecycle of MCP servers, which consists of three key phases: creation, operation, and update. We analyze the security and privacy risks associated with each phase and propose strategies to mitigate potential threats. The paper also examines the current MCP landscape, including its adoption by industry leaders and various use cases, as well as the tools and platforms supporting its integration. We explore future directions for MCP, highlighting the challenges and opportunities that will influence its adoption and evolution within the broader AI ecosystem. Finally, we offer recommendations for MCP stakeholders to ensure its secure and sustainable development as the AI landscape continues to evolve.

Metamaterials, renowned for their exceptional mechanical, electromagnetic, and thermal properties, hold transformative potential across diverse applications, yet their design remains constrained by labor - intensive trial - and - error methods and limited data interoperability. Here, we introduce CrossMatAgent -- a novel multi - agent framework that synergistically integrates large language models with state - of - the - art generative AI to revolutionize metamaterial design. By orchestrating a hierarchical team of agents -- e ach specializing in tasks such as pattern analysis, architectural synthesis, prompt engineering, and supervisory feedback -- our system leverages the multimodal reasoning of GPT - 4o alongside the generative precision of DALL - E 3 and a fine - tuned Stable Diffusion Extra Large ( XL) model. This integrated approach automates data augmentation, enhances design fidelity, and produces simulation - and 3D printing - ready metamaterial patterns. Comprehensive evaluations, including Contrastive Language - Image Pre - training ( C LIP) - based alignment, SHAP ( SHapley Additive exPlanations) interpretability analyses, and mechanical simulations under varied load conditions, demonstrate the framework's ability to generate diverse, reproducible, and application - ready designs . CrossMatAgent thus establishes a scalable, AI - driven paradigm that bridges the gap between conceptual innovation and practical realization, paving the way for accelerated metamaterial development.

Meta has released the first two models from its multimodal Llama 4 suite: LLama 4 Scout and Llama 4 Maverick. Maverick is "the workhorse" of the two and excels at image and text understanding for "general assistant and chat use cases," the company said in a blog post, while the smaller model Scout could tackle things like "multi-document summarization, parsing extensive user activity for personalized tasks, and reasoning over vast codebases." The company also introduced Llama 4 Behemoth, an upcoming model it says is "among the world's smartest LLMs" -- and CEO Mark Zuckerberg said we'll be hearing about a fourth model, LLama 4 Reasoning, "in the next month." Both Maverick and Scout are available to download now from the LLama website and Hugging Face, and they've been added to Meta AI, including for WhatsApp, Messenger and Instagram DMs. Scout has 17 billion active parameters with 16 experts, Meta says.

Tricia Helfer, who played a humanoid robot Cylon on "Battlestar Galactica," says the show's look at the conflict between humans and AI still resonates today. THE FUTURE IS NOW: "Battlestar Galactica" star Tricia Helfer feels the show was a prescient warning about artificial intelligence when it debuted more than 20 years ago. DEMOCRATIZING INTELLIGENCE: Compute Exchange CEO Simeon Bochev weighed in on the impact of computing power in artificial intelligence during an appearance on "Mornings with Maria." SIDE-FLIPPING ROBOT: Robots aren't just efficient machines anymore, they are now agile performers that can flip and jog. MAJOR INVESTMENT: ChatGPT creator OpenAI on Monday revealed it is getting up to 40 billion in new funding.

The opioid overdose epidemic remains a critical public health crisis, particularly in the United States, leading to significant mortality and societal costs. Social media platforms like Reddit provide vast amounts of unstructured data that offer insights into public perceptions, discussions, and experiences related to opioid use. This study leverages Natural Language Processing (NLP), specifically Opioid Named Entity Recognition (ONER-2025), to extract actionable information from these platforms. Our research makes four key contributions. First, we created a unique, manually annotated dataset sourced from Reddit, where users share self-reported experiences of opioid use via different administration routes. This dataset contains 331,285 tokens and includes eight major opioid entity categories. Second, we detail our annotation process and guidelines while discussing the challenges of labeling the ONER-2025 dataset. Third, we analyze key linguistic challenges, including slang, ambiguity, fragmented sentences, and emotionally charged language, in opioid discussions. Fourth, we propose a real-time monitoring system to process streaming data from social media, healthcare records, and emergency services to identify overdose events. Using 5-fold cross-validation in 11 experiments, our system integrates machine learning, deep learning, and transformer-based language models with advanced contextual embeddings to enhance understanding. Our transformer-based models (bert-base-NER and roberta-base) achieved 97% accuracy and F1-score, outperforming baselines by 10.23% (RF=0.88).

While improving Microsoft Copilot's basic research functions may not be as exciting as, say, a Copilot that dishes out compliments and knows everything about you, it's still incredibly useful for Copilot as a research tool. On Friday, at Microsoft's 50th anniversary celebration, the company showed off its Copilot redesign. The company introduced Copilot Vision for Windows as well as a more intuitive Copilot assistant. But Copilot's knowledge capabilities are also being improved with Copilot Search, Deep Research, podcasts, and Pages. The podcasts and Pages features may seem familiar.

They want to build AI models that achieve "artificial general intelligence," or AGI--matching or exceeding the capabilities of the human mind. The difference between these two men is that Altman has suggested that his company, OpenAI, has practically built the technology already. Chollet, a French computer scientist and one of the industry's sharpest skeptics, has said that notion is "absolutely clown shoes." When I spoke with him earlier this year, Chollet told me that AI companies have long been "intellectually lazy" in suggesting that their machines are on the path to a kind of supreme knowledge. At this point, those claims are based largely on the programs' ability to pass specific tests (such as the LSAT, Advanced Placement Biology, and even an introductory sommelier exam).