Leveraging large language models (LLMs) to generate high-stakes documents, such as informed consent forms (ICFs), remains a significant challenge due to the extreme need for regulatory compliance and factual accuracy. Here, we present InformGen, an LLM-driven copilot for accurate and compliant ICF drafting by optimized knowledge document parsing and content generation, with humans in the loop. We further construct a benchmark dataset comprising protocols and ICFs from 900 clinical trials. Experimental results demonstrate that InformGen achieves near 100% compliance with 18 core regulatory rules derived from FDA guidelines, outperforming a vanilla GPT-4o model by up to 30%. Additionally, a user study with five annotators shows that InformGen, when integrated with manual intervention, attains over 90% factual accuracy, significantly surpassing the vanilla GPT-4o model's 57%-82%. Crucially, InformGen ensures traceability by providing inline citations to source protocols, enabling easy verification and maintaining the highest standards of factual integrity.

It examines fair use legal arguments and eight distinct substantive arguments, contending that every legal and substantive argument favoring fair use for GenAI applies equally, if not more so, to humans. Therefore, granting GenAI exceptional privileges in this domain is legally and logically inco nsistent with withholding broad fair use exemptions from individual humans.

The letter asked Lisa Nandy, the secretary of state for culture, media and sport, to summon Meta senior executives to parliament. "There is a longstanding contractual obligation that when third parties make use of an author's work they compensate us," said McDermid when asked why she signed the letter. "Adaptation, translation, photocopying – they all accept the duty to recompense us for making their work possible. "I'm a crime writer – I understand theft when I see it. And by using pirated material, Meta are stealing from us twice over.

Elon Musk's xAI is facing a potential trademark dispute over the name of its chatbot, Grok. The company's trademark application with the US Patent and Trademark Office has been suspended after the agency argued the name could be confused with that of two other companies, AI chipmaker Groq and software provider Grokstream. Now, a third tech startup called Bizly is claiming it owns the rights to "Grok." This isn't the first time Musk has chosen a name for one of his products that other companies say they trademarked first. Last month, Musk's social media platform settled a lawsuit brought by a marketing firm that claimed it owns exclusive rights to the name X. Bizly and xAI appear to have arrived at the name Grok independently.

In response to an executive order, President Donald Trump's team will present him with a plan for creating the Golden Dome, a missile defense shield meant to guard against attacks that are increasingly difficult to defeat. This effort will demand innovative thinking, collective will and rapid action. Since my tenure as director of the Missile Defense Agency in the early 2000s, an integrated network of sensors based in space, land and sea paired with ground-based interceptors has effectively deterred rudimentary missile attacks on our homeland from Iran, North Korea and others. But as they continue to improve their capabilities and as we look at a resurgent Russia and aggressive China, we need to build our next-generation missile defense. The window to defeat ballistic missiles heading to targets in the US is less than 40 minutes and can be as brief as 10 or 15 minutes if launched from a submarine closer to its target.

Kulim, Malaysia – When tech giant AT&S decided a few years ago that it needed to ramp up production to keep pace with the artificial intelligence (AI) boom, it did not look to its largest manufacturing facilities in China. The Austrian firm's plants in Chongqing and Shanghai – opened in 2022 and 2016, respectively – employ some 9,000 workers between them, churning out high-end components used in everything from consumer electronics to cars. But AT&S was at the same time coming to grips with the risks of concentrating production in one country. Like many tech firms grappling with the disruption of the COVID-19 pandemic and the trade war salvoes between the United States and China, AT&S decided it needed to diversify its supply chains. Malaysia quickly emerged at the top of the company's list of potential locations for its next plant.

AI tools will soon be in use in classrooms across England, but the education secretary, Bridget Phillipson, has one big question she wants answered: will they save time? Attending a Department for Education-sponsored hackathon in central London last week, Phillipson listened as developers explained how their tools could compile pupil reports, improve writing samples and even assess the quality of soldering done by trainee electrical engineers. After listening to one developer extol their AI writing analysis tool as "superhuman", able to aggregate all the writing a pupil had ever done, Phillipson asked bluntly: "Do you know how much time it will have saved?" That will be our next step, the developer admitted, less confidently. In an interview with the Guardian, Phillipson said her interest in AI was less futuristic and more practical.

This short paper puts forward a vision for a new democratic model enabled by the recent technological advances in agentic AI. It therefore opens with drawing a clear and concise picture of the model, and only later addresses related proposals and research directions, and concerns regarding feasibility and safety. It ends with a note on the timeliness of this idea and on optimism. The model proposed is that of assigning each citizen an AI Agent that would serve as their political delegate, enabling the return to direct democracy. The paper examines this models relation to existing research, its potential setbacks and feasibility and argues for its further development.

Retrieval Augmented Generation (RAG) systems have emerged as a powerful method for enhancing large language models (LLMs) with up-to-date information. However, the retrieval step in RAG can sometimes surface documents containing contradictory information, particularly in rapidly evolving domains such as news. These contradictions can significantly impact the performance of LLMs, leading to inconsistent or erroneous outputs. This study addresses this critical challenge in two ways. First, we present a novel data generation framework to simulate different types of contradictions that may occur in the retrieval stage of a RAG system. Second, we evaluate the robustness of different LLMs in performing as context validators, assessing their ability to detect contradictory information within retrieved document sets. Our experimental results reveal that context validation remains a challenging task even for state-of-the-art LLMs, with performance varying significantly across different types of contradictions. While larger models generally perform better at contradiction detection, the effectiveness of different prompting strategies varies across tasks and model architectures. We find that chain-of-thought prompting shows notable improvements for some models but may hinder performance in others, highlighting the complexity of the task and the need for more robust approaches to context validation in RAG systems.

Urban systems are managed using complex textual documentation that need coding and analysis to set requirements and evaluate built environment performance. This paper contributes to the study of applying large-language models (LLM) to qualitative coding activities to reduce resource requirements while maintaining comparable reliability to humans. Qualitative coding and assessment face challenges like resource limitations and bias, accuracy, and consistency between human evaluators. Here we report the application of LLMs to deductively code 10 case documents on the presence of 17 digital twin characteristics for the management of urban systems. We utilize two prompting methods to compare the semantic processing of LLMs with human coding efforts: whole text analysis and text chunk analysis using OpenAI's GPT-4o, GPT-4o-mini, and o1-mini models. We found similar trends of internal variability between methods and results indicate that LLMs may perform on par with human coders when initialized with specific deductive coding contexts. GPT-4o, o1-mini and GPT-4o-mini showed significant agreement with human raters when employed using a chunking method. The application of both GPT-4o and GPT-4o-mini as an additional rater with three manual raters showed statistically significant agreement across all raters, indicating that the analysis of textual documents is benefited by LLMs. Our findings reveal nuanced sub-themes of LLM application suggesting LLMs follow human memory coding processes where whole-text analysis may introduce multiple meanings. The novel contributions of this paper lie in assessing the performance of OpenAI GPT models and introduces the chunk-based prompting approach, which addresses context aggregation biases by preserving localized context.