Tackling One Health Risks: How Large Language Models are leveraged for Risk Negotiation and Consensus - building. Study Centre for Land-use related Evaluation procedures, One-Health, German Federal Institute for Risk Assessment, Berlin, Germany; Email: Maciej.Durkalec@bfr.bund.de Faculty of Bioscience Engineering, Department. of Food Technology, Safety and Health, Ghent University, Ghent, Belgium, E - mail: Andreja.Rajkovic@UGent.be Abstract Key global challenges of our times are characterized by complex interdependencies and can only be effectively addressed through an integrated, participatory effort. Conventional risk analysis frameworks often reduce complexity to ensure manageability, crea ting silos that hinder comprehensive solutions. A fundamental shift towards holistic strategies is essential to enable effective negotiations between different sectors and to balance the competing interests of stakeholders. However, achieving this balance is often hindered by limited time, vast amounts of information, and the complexity of integrating diverse perspectives. This study presents an AI - assisted negotiation framework that incorporates large language models (LLMs) and AI - based autonomous agents i nto a negotiation - centered risk analysis workflow. The framework enables stakeholders to simulate negotiations, systematically model dynamics, anticipate compromises, and evaluate solution impacts. By leveraging LLMs' semantic analysis capabilities we coul d mitigate information overload and augment decision - making process under time constraints. Proof - of - concept implementations were conducted in two real - world scenarios: (i) prudent use of a biopesticide, and (ii) targeted wild animal population control. Ou r work demonstrates the potential of AI - assisted negotiation to address the current lack of tools for cross - sectoral engagement.

Third, the current game state data--number of players, last played card, hand contents, next player, recent moves, and legal actions. Finally, the LLM was asked to choose the best action according to the specified prompting method. The game state information was extracted from RLCard and reformatted for readability. While RLCard encodes cards using shorthand (e.g., "r-5" for red 5), we expanded these into full descriptions to improve the model's comprehension. An example of the complete prompt format is shown in Figure 3. To drive the model's action selection, we applied two prompting strategies inspired by Moore et al. [17]: cloze prompting and counterfactual prompting. These methods determine how the model interprets the prompt and evaluates its legal actions during gameplay. Cloze Prompting: In this method, legal actions were labeled with sequential letters (A, B, C, etc.), and the LLM was instructed to choose the letter corresponding to the best move. Only one token was allowed in the output, and the highest-probability token from the set of allowable actions was selected as the action.

Computational models have emerged as powerful tools for energy modeling research, touting scalability and quantitative results. However, these models require a plethora of data, some of which is inaccessible, expensive, or raises privacy concerns. We introduce a modular multimodal framework to produce this data from publicly accessible residential information and images using generative artificial intelligence (AI). Additionally, we provide a pipeline demonstrating this framework, and we evaluate its generative AI components. Our experiments show that our framework's use of AI avoids common issues with generative models. Our framework produces realistic, labeled data. By reducing dependence on costly or restricted data sources, we pave a path towards more accessible and reproducible research.

Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs) by offloading most layers to a central server while keeping in- and output layers on the client-side. This setup enables SL to leverage server computation capacities without sharing data, making it highly effective in resource-constrained environments dealing with sensitive data. However, the distributed nature enables malicious clients to manipulate the training process. By sending poisoned intermediate gradients, they can inject backdoors into the shared DNN. Existing defenses are limited by often focusing on server-side protection and introducing additional overhead for the server. A significant challenge for client-side defenses is enforcing malicious clients to correctly execute the defense algorithm. We present ZORRO, a private, verifiable, and robust SL defense scheme. Through our novel design and application of interactive zero-knowledge proofs (ZKPs), clients prove their correct execution of a client-located defense algorithm, resulting in proofs of computational integrity attesting to the benign nature of locally trained DNN portions. Leveraging the frequency representation of model partitions enables ZORRO to conduct an in-depth inspection of the locally trained models in an untrusted environment, ensuring that each client forwards a benign checkpoint to its succeeding client. In our extensive evaluation, covering different model architectures as well as various attack strategies and data scenarios, we show ZORRO's effectiveness, as it reduces the attack success rate to less than 6\% while causing even for models storing \numprint{1000000} parameters on the client-side an overhead of less than 10 seconds.

Question answering (QA) plays a central role in financial education, yet existing large language model (LLM) approaches often fail to capture the nuanced and specialized reasoning required for financial problem-solving. The financial domain demands multistep quantitative reasoning, familiarity with domain-specific terminology, and comprehension of real-world scenarios. We present a multi-agent framework that leverages role-based prompting to enhance performance on domain-specific QA. Our framework comprises a Base Generator, an Evidence Retriever, and an Expert Reviewer agent that work in a single-pass iteration to produce a refined answer. We evaluated our framework on a set of 3,532 expert-designed finance education questions from Study.com, an online learning platform. We leverage retrieval-augmented generation (RAG) for contextual evidence from 6 finance textbooks and prompting strategies for a domain-expert reviewer. Our experiments indicate that critique-based refinement improves answer accuracy by 6.6-8.3% over zero-shot Chain-of-Thought baselines, with the highest performance from Gemini-2.0-Flash. Furthermore, our method enables GPT-4o-mini to achieve performance comparable to the finance-tuned FinGPT-mt_Llama3-8B_LoRA. Our results show a cost-effective approach to enhancing financial QA and offer insights for further research in multi-agent financial LLM systems.

Generative AI is being massively deployed in digital services, at a scale that will result in significant environmental harm. We document how tech companies are transforming established user interfaces to impose AI use and show how and to what extent these strategies fit within established deceptive pattern categories. We identify two main design strategies that are implemented to impose AI use in both personal and professional contexts: imposing AI features in interfaces at the expense of existing non-AI features and promoting narratives about AI that make it harder to resist using it. We discuss opportunities for regulating the imposed adoption of AI features, which would inevitably lead to negative environmental effects.

Plus: ICE deploys secretive phone surveillance tech, officials warn of Chinese surveillance tools in US highway infrastructure, and more. Right-wing internet personality and Turning Point USA cofounder Charlie Kirk was shot and killed on Wednesday during a speaking engagement at Utah Valley University in Orem, Utah. After a chaotic 24-hour manhunt, the FBI named 22-year-old Utah resident Tyler Robinson as a suspect in the murder. As polarization and political violence continues to increase in the US, a new platform from the Public Service Alliance is offering tools like data-removal services and threat monitoring to public servants who increasingly need to defend themselves and their data. Meanwhile, new research this week warned that the number of US investors putting money into invasive commercial spyware rose significantly in 2024.

Cancel Culture Comes for Artists Who Posted About Charlie Kirk's Death A episode was taken off air, a DC comic series was canceled, and several artists were fired in the aftermath of the shooting. Almost immediately after she posted about the shooting of Charlie Kirk, author and transwoman Gretchen Felker-Martin started having second thoughts. Felker-Martin, who wrote the latest iteration of DC Comics' series, said "thoughts and prayers you Nazi bitch" on Bluesky in response to the killing of Kirk, a right-wing influencer and Trump ally who was staunchly anti-trans rights. "Hope the bullet's okay after touching Kirk," she added. Kirk died after being shot at a stop on his American Comeback Tour organized by the conservative youth organization he founded, Turning Point USA.

Bullets Found After the Charlie Kirk Shooting Carried Messages. The inscriptions on bullets recovered near the scene of Charlie Kirk's murder appear to reference video games like and online furry role-play, not a legible political ideology. On Friday, Tyler Robinson, a 22-year-old Utah native, was identified by federal law enforcement as a suspect in the murder of Charlie Kirk . During Friday's press conference, officials said that several bullet casings recovered from a hunting rifle found near the crime scene had messages inscribed on them. During the press conference, officials appeared to take the inscriptions literally, to the extent they ascribed meaning to them at all.

Red-flag speech Charlie Kirk's'assassin' gave at family dinner Stephen King deletes post and apologizes for his'lies' about Charlie Kirk Fired MSNBC analyst DEFENDS vile Charlie Kirk comments as network bosses address'unacceptable' on-air remarks Grandma of Charlie Kirk's'assassin' breaks silence to reveal why the FBI MUST have the wrong man Denise Richards' ex Aaron Phypers shares photos of their $3.5M mansion that is CLUTTERED with junk I ran the Trump shooting task force. I was shocked by all the security blunders at Charlie Kirk's event... here are the crucial steps they missed Outrage erupts after Amazon sells book about Charlie Kirk's assassination HOURS after the shooting SCOTT JENNINGS: My friend Charlie Kirk is dead because of the Left's hateful speech... and they dare tell us to be silent. Tearful meteorologist's emotional plea to fans as it is revealed she's been laid off Shoppers rushing to Dollar Tree for $1.50 bargain they say looks straight out of Williams Sonoma AMANDA PLATELL: William and Kate have become the Prince and Princess of Boring. This is why they need to stop skulking around and take a leaf out of Harry's book - or risk Charles's wrath'They could be married before Christmas': Chris Hughes' father reveals what the family really think of his son's controversial relationship with JoJo Siwa... and why couple could be about to take huge step Bannon torches Kash Patel for undeserved'glazing' after Kirk killer turned HIMSELF in Outrage erupts after Amazon sells book about Charlie Kirk's assassination HOURS after the shooting Outrage and online conspiracy theories spread after a book about Charlie Kirk's assassination was listed for sale on Amazon. On Wednesday, social media users shared images of a book available on Amazon titled ' The Shooting of Charlie Kirk: A Comprehensive Account of the Utah Valley University Attack, the Aftermath, and America's Response.'