Since Multimodal Large Language Models (MLLMs) are increasingly being integrated into everyday tools and intelligent agents, growing concerns have arisen regarding their possible output of unsafe contents, ranging from toxic language and biased imagery to privacy violations and harmful misinformation. Current safety benchmarks remain highly limited in both modality coverage and performance evaluations, often neglecting the extensive landscape of content safety. In this work, we introduce OutSafe-Bench, the first most comprehensive content safety evaluation test suite designed for the multimodal era. OutSafe-Bench includes a large-scale dataset that spans four modalities, featuring over 18,000 bilingual (Chinese and English) text prompts, 4,500 images, 450 audio clips and 450 videos, all systematically annotated across nine critical content risk categories. In addition to the dataset, we introduce a Multidimensional Cross Risk Score (MCRS), a novel metric designed to model and assess overlapping and correlated content risks across different categories. T o ensure fair and robust evaluation, we propose FairScore, an explainable automated multi-reviewer weighted aggregation framework. FairScore selects top-performing models as adaptive juries, thereby mitigating biases from single-model judgments and enhancing overall evaluation reliability. Our evaluation of nine state-of-the-art MLLMs reveals persistent and substantial safety vulnerabilities, underscoring the pressing need for robust safeguards in MLLMs. W arning: This paper may contain some offensive content in data and model outputs.

This study investigates the efficacy of using multimodal machine learning techniques to detect deception in dyadic interactions, focusing on the integration of data from both the deceiver and the deceived. We compare early and late fusion approaches, utilizing audio and video data - specifically, Action Units and gaze information - across all possible combinations of modalities and participants. Our dataset, newly collected from Swedish native speakers engaged in truth or lie scenarios on emotionally relevant topics, serves as the basis for our analysis. The results demonstrate that incorporating both speech and facial information yields superior performance compared to single-modality approaches. Moreover, including data from both participants significantly enhances deception detection accuracy, with the best performance (71%) achieved using a late fusion strategy applied to both modalities and participants. These findings align with psychological theories suggesting differential control of facial and vocal expressions during initial interactions. As the first study of its kind on a Scandinavian cohort, this research lays the groundwork for future investigations into dyadic interactions, particularly within psychotherapy settings.

Machine unlearning aims to remove the influence of specific data points from a trained model to satisfy privacy, copyright, and safety requirements. In real deployments, providers distribute a global model to many edge devices, where each client personalizes the model using private data. When a deletion request is issued, clients may ignore it or falsely claim compliance, and providers cannot check their parameters or data. This makes verification difficult, especially because personalized models must forget the targeted samples while preserving local utility, and verification must remain lightweight on edge devices. We introduce ZK APEX, a zero-shot personalized unlearning method that operates directly on the personalized model without retraining. ZK APEX combines sparse masking on the provider side with a small Group OBS compensation step on the client side, using a blockwise empirical Fisher matrix to create a curvature-aware update designed for low overhead. Paired with Halo2 zero-knowledge proofs, it enables the provider to verify that the correct unlearning transformation was applied without revealing any private data or personalized parameters. On Vision Transformer classification tasks, ZK APEX recovers nearly all personalization accuracy while effectively removing the targeted information. Applied to the OPT125M generative model trained on code data, it recovers around seventy percent of the original accuracy. Proof generation for the ViT case completes in about two hours, more than ten million times faster than retraining-based checks, with less than one gigabyte of memory use and proof sizes around four hundred megabytes. These results show the first practical framework for verifiable personalized unlearning on edge devices.

Reinsurance decision-making exhibits the core structural properties that motivate multi-agent models: distributed and asymmetric information, partial observability, heterogeneous epistemic responsibilities, simulator-driven environment dynamics, and binding prudential and regulatory constraints. Deterministic workflow automation cannot meet these requirements, as it lacks the epistemic flexibility, cooperative coordination mechanisms, and norm-sensitive behaviour required for institutional risk-transfer. We propose the Reinsurance Constrained Multi-Agent Simulation Process (R-CMASP), a formal model that extends stochastic games and Dec-POMDPs by adding three missing elements: (i) simulator-coupled transition dynamics grounded in catastrophe, capital, and portfolio engines; (ii) role-specialized agents with structured observability, belief updates, and typed communication; and (iii) a normative feasibility layer encoding solvency, regulatory, and organizational rules as admissibility constraints on joint actions. Using LLM-based agents with tool access and typed message protocols, we show in a domain-calibrated synthetic environment that governed multi-agent coordination yields more stable, coherent, and norm-adherent behaviour than deterministic automation or monolithic LLM baselines--reducing pricing variance, improving capital efficiency, and increasing clause-interpretation accuracy. Embedding prudential norms as admissibility constraints and structuring communication into typed acts measurably enhances equilibrium stability. Overall, the results suggest that regulated, simulator-driven decision environments are most naturally modelled as norm-governed, simulator-coupled multi-agent systems.

OpenAI and its largest financial backer, Microsoft, have been sued in California state court over claims that ChatGPT, OpenAI's popular chatbot, encouraged a man with mental illnesses to kill his mother and himself. The lawsuit, filed on Thursday, said that ChatGPT fuelled 56-year-old Stein-Erik Soelberg's delusions of a vast conspiracy against him, and eventually led him to murder his 83-year-old mother, Suzanne Adams, in Connecticut in August. The case, filed by Adams's estate, is among a small but growing number of lawsuits filed against artificial intelligence companies claiming that their chatbots encouraged suicide. It is the first wrongful death litigation involving an AI chatbot that has targeted Microsoft, and the first to tie a chatbot to a homicide rather than a suicide. It is seeking an undetermined amount of money damages and an order requiring OpenAI to install safeguards in ChatGPT.

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This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by Refinitiv Lipper .

It's no accident the company picked a partner it could control. This morning Disney and OpenAI announced a three-year licensing agreement: Starting in 2026, ChatGPT and Sora can generate images and videos incorporating Disney IP, including more than 200 characters from the company's stable of Star Wars, Pixar and Marvel brands. To say these companies make for strange bedfellows is an understatement. Before OpenAI released Sora, the company reportedly notified studios and talent agencies they would need to opt out of having their work appear in the new app. The law effectively froze the advancement of the public domain in the United States, with Disney being the greatest beneficiary. On the face of it, it's unclear OpenAI is getting much value out of the deal.

It's rude to boast, but here in 2025, you've got to take the wins where you can get them. This morning, magazine announced its Person of the Year, and it's me. If you want to get all technical about it, 's Person of the Year is not a person at all but a collection of people: the architects of AI. One of the two covers released is a re-creation of the "Lunch Atop a Skyscraper" photograph from 1932, which depicted blue-collar ironworkers suspended hundreds of feet in the air during the construction of 30 Rockefeller Plaza. In its image, replaces these laborers with tech personalities such as Mark Zuckerberg, Elon Musk, Sam Altman, and Jensen Huang.

'47 Ronin' director found guilty of defrauding Netflix out of $11 million Carl Rinsch faces up to 90 years in prison. A director who was charged with defrauding Netflix out of millions of dollars has been found guilty, reports . Carl Rinsch, director of the 2013 Keanu Reeves movie, now faces up to 90 years in prison. Rinsch began filming the project, (later renamed), around 2017. (Its premise: A scientist creates an organic humanoid species that turns on its creators.) The director completed six short-form episodes with his own money and investor funds.