OpenAI confirms personal information from API accounts was exposed in a data breach involving third-party analytics provider Mixpanel, affecting names and email addresses.

Parent company Microsoft has already recognized the effort. Id Software, the company behind Doom, has voted in favor of forming a wall-to-wall union. The term wall-to-wall refers to a union that includes every employee, regardless of duties. The vote wasn't unanimous, though a majority did vote in favor of the union. The union will work in conjunction with the Communications Workers of America (CWA), which is the same organization involved with parent company ZeniMax's recent unionization efforts .

Get in, loser, we're going judging. Honestly, compiling the biggest losers for Engadget is more fun than talking up the winners . While we reviewed nothing as atrocious as those ill-fated AI assistant gadgets from 2024, AI companies and services straddled both the winner and loser podiums. The losers might be you, the American consumer. In the US, anyone wanting a drone will have to find something that isn't made by DJI.

OpenAI and its investor Microsoft have been sued over a Connecticut murder-suicide in the latest case to blame ChatGPT for dangerous psychological manipulation of users. OpenAI and its investor, Microsoft, have been sued over a Connecticut murder-suicide in the latest case to blame the popular ChatGPT chatbot for dangerous psychological manipulation of users. The lawsuit turns on the actions of a 56-year-old man who lived with his 83-year-old mother in Greenwich, Connecticut, and had been conversing for months with the chatbot over his fear that he was under surveillance and people were trying to kill him. In August, according to police and the state medical examiner, Stein-Erik Soelberg killed his mother, Suzanne Adams, then took his own life. Soelberg's dialogue with ChatGPT convinced him that he had made the chatbot conscious, and that he had been implanted with a "divine instrument system" in his neck and brain, which related to a "divine mission," according to a complaint filed Thursday in California Superior Court in San Francisco, where OpenAI is based.

The administration will also attempt to prevent states with "onerous" AI laws from accessing broadband funding. WASHINGTON, DC - DECEMBER 11: U.S. President Donald Trump displays a signed executive order in the Oval Office of the White House on December 11, 2025 in Washington, DC. The executive order curbs states' ability to regulate artificial intelligence, something for which the tech industry has been lobbying. On Thursday evening, President Donald Trump signed an executive order calling for a single, nationwide regulatory framework governing artificial intelligence at the expense of the ability of different states to regulate the nascent technology. "To win, United States AI companies must be free to innovate without cumbersome regulation," the order states. As was expected after a draft of the order leaked earlier this week, the centerpiece of the document is an "AI Litigation Task Force whose sole responsibility shall be to challenge state AI laws inconsistent" with the president's policy vision.

The order creates a Justice Department task force to challenge state AI laws and directs the Commerce Department to pull future broadband funding from states that pass "onerous" legislation. President Donald Trump signed a highly anticipated executive order on Thursday that sets in motion a plan to establish a national regulatory framework for artificial intelligence while undercutting states' abilities to enact their own rules. The order, titled "Ensuring a National Policy Framework for Artificial Intelligence," creates an AI litigation task force within the Justice Department to directly challenge state AI laws the administration finds to conflict with federal policy. It also directs the Department of Commerce to craft guidelines that could make states ineligible for future broadband funding if they pass "onerous" AI laws. The push for sweeping federal preemption of state AI laws has largely been fueled by AI investors, conservative policy shops, and tech industry trade groups.

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.