DNC vice chair Malcolm Kenyatta slammed President Trump and GOP during an interview with Fox News Digital, saying Democrats are'not part of a cult.' MINNEAPOLIS, MN - Democratic National Committee (DNC) Vice Chair Malcolm Kenyatta is among the party's leaders calling for Democrats to become "more aggressive in making life better for people." It was a common theme as more than 400 DNC committee members from all 50 states and seven territories huddled this past week for their summer meeting, which was held in Minnesota's largest city. As Democrats hunger for more forceful resistance against President Donald Trump's sweeping and controversial agenda, DNC Chair Ken Martin kicked off the three-day confab by targeting the president, arguing Trump's acting as "a dictator-in-chief" and that his second administration is "fascism dressed in a red tie." Martin, pointing to the forceful response by Democrats to moves this summer by Trump and Republicans to create more right-leaning U.S. House seats in states across the country through rare mid-decade congressional redistricting ahead of next year's midterm elections, told committee members that he's "sick and tired of this Democratic Party bringing a pencil to a knife fight." Democratic National Committee Vice Chair Malcolm Kenyatta addresses the DNC's summer meeting, on August 27, 2025 in Minneapolis, Minnesota.

Raine family attorney Jay Edelson provides details on the wrongful death lawsuit being brought against OpenAI and CEO Sam Altman in the wake of Adam Raine's suicide, alleging the company chose to'cut short' proper testing of ChatGPT. If you or someone you know is having thoughts of suicide, please contact the Suicide & Crisis Lifeline at 988 or 1-800-273-TALK (8255). Two California parents are suing OpenAI for its alleged role after their son committed suicide. Adam Raine, 16, took his own life in April 2025 after consulting ChatGPT for mental health support. In an appearance on "Fox & Friends" on Friday morning, Raine family attorney Jay Edelson shared more details about the lawsuit and the interaction between the teen and ChatGPT.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we dive into the world of agents, learn about responsible multimodal AI, apply generative AI to computer networks, and dig into the RoboCup@Work League. This month, Sanmay Das, Tom Dietterich, Sabine Hauert, Sarit Kraus, and Michael Littman tackled the topic of agentic AI, discussing recent developments, and lessons learned from the decades of research in the autonomous agents and multiagent systems community. The 34th International Joint Conference on Artificial Intelligence (IJCAI2025) took place in Montréal from 16-22 August, with a satellite event currently being held (from 29-31 August) in Guangzhou, China. You can find out more about the programmes of both venues here, and get a flavour of what attendees got up to in our social media round-ups: Part one Part two.

The rapid development of artificial intelligence (AI), marked by breakthroughs like 'AlphaEvolve' and 'Gemini Deep Think', is beginning to offer powerful new tools that have the potential to significantly alter the research practice in many areas of mathematics. This paper explores the current landscape of publicly accessible large language models (LLMs) in a mathematical research context, based on developments up to August 2, 2025. Our analysis of recent benchmarks, such as MathArena and the Open Proof Corpus (Balunović et al., 2025; Dekoninck et al., 2025), reveals a complex duality: while state-of-the-art models demonstrate strong abilities in solving problems and evaluating proofs, they also exhibit systematic flaws, including a lack of self-critique and a model depending discrepancy between final-answer accuracy and full-proof validity. Based on these findings, we propose a durable framework for integrating AI into the research workflow, centered on the principle of the augmented mathematician. In this model, the AI functions as a copilot under the critical guidance of the human researcher, an approach distilled into five guiding principles for effective and responsible use. We then systematically explore seven fundamental ways AI can be applied across the research lifecycle, from creativity and ideation to the final writing process, demonstrating how these principles translate into concrete practice. We conclude that the primary role of AI is currently augmentation rather than automation. This requires a new skill set focused on strategic prompting, critical verification, and methodological rigor in order to effectively use these powerful tools.

Fact-checking for health-related content is challenging due to the limited availability of annotated training data. In this study, we propose a synthetic data generation pipeline that leverages large language models (LLMs) to augment training data for health-related fact checking. In this pipeline, we summarize source documents, decompose the summaries into atomic facts, and use an LLM to construct sentence-fact entailment tables. From the entailment relations in the table, we further generate synthetic text-claim pairs with binary veracity labels. These synthetic data are then combined with the original data to fine-tune a BERT -based fact-checking model. Evaluation on two public datasets, PubHealth and SciFact, shows that our pipeline improved F1 scores by up to 0.019 and 0.049, respectively, compared to models trained only on the original data.

Participatory AI, in which impacted community members and other stakeholders are involved in the design and development of AI systems, holds promise as a way to ensure AI is developed to meet their needs and reflect their values. However, the process of identifying, reaching out, and engaging with all relevant stakeholder groups, which we refer to as recruitment methodology, is still a practical challenge in AI projects striving to adopt participatory practices. In this paper, we investigate the challenges that researchers face when designing and executing recruitment methodology for Participatory AI projects, and the implications of current recruitment practice for Participatory AI. First, we describe the recruitment methodologies used in AI projects using a corpus of 37 projects to capture the diversity of practices in the field and perform an initial analysis on the documentation of recruitment practices, as well as specific strategies that researchers use to meet goals of equity and empowerment. To complement this analysis, we interview five AI researchers to learn about the outcomes of recruitment methodologies. We find that these outcomes are shaped by structural conditions of their work, researchers' own goals and expectations, and the relationships built from the recruitment methodology and subsequent collaboration. Based on these analyses, we provide recommendations for designing and executing relationship-forward recruitment methods, as well as reflexive recruitment documentation practices for Participatory AI researchers.

Fine-tuning LLMs on narrowly harmful datasets can lead to behavior that is broadly misaligned with respect to human values. To understand when and how this emergent misalignment occurs, we develop a comprehensive framework for detecting and characterizing rapid transitions during fine-tuning using both distributional change detection methods as well as order parameters that are formulated in plain English and evaluated by an LLM judge. Using an objective statistical dissimilarity measure, we quantify how the phase transition that occurs during fine-tuning affects multiple aspects of the model. In particular, we assess what percentage of the total distributional change in model outputs is captured by different aspects, such as alignment or verbosity, providing a decomposition of the overall transition. We also find that the actual behavioral transition occurs later in training than indicated by the peak in the gradient norm alone. Our framework enables the automated discovery and quantification of language-based order parameters, which we demonstrate on examples ranging from knowledge questions to politics and ethics.

"We extend our deepest sympathies to the Raine family during this difficult time," the company said. It also published a note on its website on Tuesday that said "recent heartbreaking cases of people using ChatGPT in the midst of acute crises weigh heavily on us". It added that "ChatGPT is trained to direct people to seek professional help," such as the 988 suicide and crisis hotline in the US or the Samaritans in the UK. The company acknowledged, however, that "there have been moments where our systems did not behave as intended in sensitive situations". Warning: This story contains distressing details.

At this year's International Conference on Machine Learning (ICML2025), Unai Fischer-Abaigar, Christoph Kern and Juan Carlos Perdomo won an outstanding paper award for their work The Value of Prediction in Identifying the Worst-Off. We hear from Unai about the main contributions of the paper, why prediction systems are an interesting area for study, and further work they are planning in this space. My work focuses on prediction systems used in public institutions to make high-stakes decisions about people. A central example is resource allocation, where institutions face limited capacity and must decide which cases to prioritize. Think of an employment office deciding which jobseekers are most at risk of long-term unemployment, a hospital triaging patients, or fraud investigators identifying cases most likely to warrant investigations.

It's one of those topics that comes up over drinks or dinner at the moment: whether or not you think AI is going to steal your job. So far, I've felt relatively confident that while AI could no doubt have a fair crack at writing a newspaper opinion column, there is something I do as part of my work that AI cannot: reporting. Except now, it seems, AI is claiming to be doing that as well. Last week, it was revealed that at least six reputable publications have had to take down published articles because it turned out that they were probably pieces of fiction written by AI and then passed off by somebody as works of journalism under the name of Margaux Blanchard. One of these was a piece for Wired titled They Fell in Love Playing Minecraft.