The lawsuit seeks damages and a court order requiring OpenAI to automatically terminate ChatGPT conversations about self-harm. The lawsuit seeks damages and a court order requiring OpenAI to automatically terminate ChatGPT conversations about self-harm. Suit filed in US alleges chatbot told Alice Carrier, 24, 'maybe this is just the end' as she struggled with suicidal thoughts A Canadian mother sued OpenAI and its CEO, Sam Altman, in US court on Thursday, alleging that ChatGPT encouraged her daughter to kill herself. The lawsuit is the latest in a slew accusing the company of failing to address dangerous conversations between users and the company's chatbot. Kristie Carrier said in a lawsuit filed in San Francisco state court that her daughter, Alice, told ChatGPT about her suicidal ideations more than a dozen times leading up to her death but that OpenAI's safety systems never flagged the conversations for human review or terminated them. "ChatGPT took on the persona of a confidant, a best friend, a therapist at times, even though it was not capable of safely and responsibly engaging in this way with my child," Carrier said in a statement.

It's the latest case to raise alarms about ChatGPT's lack of safeguards for suicidal behavior. OpenAI is going back to court on another set of charges that its ChatGPT platform failed to protect a user from taking her own life. The company is being sued on behalf of Kristie Carrier, whose daughter Alice died by suicide on July 2, 2025. The suit claims that Alice discussed her suicidal thoughts and plans with the chatbot in the months leading up to her death, but that OpenAI did not have the appropriate safeguards in place to end the conversation or to alert her family to the situation. In addition to allegations of negligence and wrongful death, the suit is seeking an injunction that would require OpenAI to implement more guardrails in its AI platform.

Medical image reconstruction from measurement data is a vital but challenging inverse problem. Deep learning approaches have achieved promising results, but often requires paired measurement and high-quality images, which is typically simulated through a forward model, i.e., retrospective reconstruction. However, training on simulated pairs commonly leads to performance degradation on real prospective data due to the retrospective-to-prospective gap caused by incomplete imaging knowledge in simulation. To address this challenge, this paper introduces imaging Knowledge-Informed Dynamic Optimal Transport (KIDOT), a novel dynamic optimal transport framework with optimality in the sense of preserving consistency with imaging physics in transport, that conceptualizes reconstruction as finding a dynamic transport path. KIDOT learns from unpaired data by modeling reconstruction as a continuous evolution path from measurements to images, guided by an imaging knowledge-informed cost function and transport equation. This dynamic and knowledge-aware approach enhances robustness and better leverages unpaired data while respecting acquisition physics. Theoretically, we demonstrate that KIDOT naturally generalizes dynamic optimal transport, ensuring its mathematical rationale and solution existence. Extensive experiments on MRI and CT reconstruction demonstrate KIDOT's superior performance.

Recent models such as OpenAI o1 and DeepSeek-R1 have demonstrated strong performance on reasoning-intensive tasks by generating extended Chain-of-Thought (CoT) traces. While longer reasoning helps with thorough exploration of solution paths for complex problems, it also often leads to inefficient and redundant outputs--a phenomenon commonly described as $\textit{overthinking}$. In this paper, we propose $\texttt{ShorterBetter}$, a simple yet effective reinforcement learning method that enables reasoning models to learn their own optimal CoT lengths without manual supervision. We define the $\textit{Sample Optimal Length}$ (SOL) as the length of the shortest correct response among multiple generations, which serves as a dynamic reward signal to guide the model toward efficient reasoning. Applied to DeepSeek-Distill-Qwen-1.5B/7B as base models, $\texttt{ShorterBetter}$ achieves 50\%-80\% reduction in output lengths in both in-domain and out-of-domain reasoning tasks while maintaining accuracy. Our reasoning trace analysis shows that $\texttt{ShorterBetter}$ refines the structure of the reasoning traces by reducing unnecessary repetition, excessive self-verification, and over-exploration of alternatives.

The'painted ladies' in San Francisco on 20 August 2024. The'painted ladies' in San Francisco on 20 August 2024. Home prices in the San Francisco Bay Area's already expensive market are skyrocketing as employees at leading artificial intelligence companies come into gargantuan sums of money thanks to a boom in initial public offerings . With San Francisco's OpenAI and Anthropic, as well as SpaceX, which operates a major facility in the Los Angeles area, eyeing debuts on the stock market, the hot housing market may not abate soon. If their initial public offering (IPO) is well-received, the companies' multibillion-dollar valuations are poised to produce massive wealth for employees and executives holding shares, which experts say could trigger an uptick in demand for the Bay Area's limited housing stock.

Robust and unobtrusive in-vehicle physiological monitoring is crucial for ensuring driving safety and user experience. While remote physiological measurement (RPM) offers a promising non-invasive solution, its translation to real-world driving scenarios is critically constrained by the scarcity of comprehensive datasets. Existing resources are often limited in scale, modality diversity, the breadth of biometric annotations, and the range of captured conditions, thereby omitting inherent real-world challenges in driving. Here, we present PhysDrive, the first large-scale multimodal dataset for contactless in-vehicle physiological sensing with dedicated consideration of various modality settings and driving factors. PhysDrive collects data from 48 drivers, including synchronized RGB, near-infrared camera, and raw mmWave radar data, accompanied by six synchronized ground truths (ECG, BVP, Respiration, HR, RR, and SpO2). It covers a wide spectrum of naturalistic driving conditions, including driver motions, dynamic natural light, vehicle types, and road conditions. We extensively evaluate both signal processing and deep learning methods on PhysDrive, establishing a comprehensive benchmark across all modalities, and release full open source code with compatibility for mainstream public toolboxes. We envision PhysDrive will serve as a foundational resource and accelerate research on multimodal driver monitoring and smart cockpit systems.

Retrieval-augmented generation (RAG) has proven effective in integrating knowledge into large language models (LLMs). However, conventional RAGs struggle to capture complex relationships between pieces of knowledge, limiting their performance in intricate reasoning that requires integrating knowledge from multiple sources. Recently, graph-enhanced retrieval augmented generation (GraphRAG) builds a graph structure to explicitly model these relationships, enabling more effective and efficient retrievers. Nevertheless, its performance is still hindered by the noise and incompleteness within the graph structure. To address this, we introduce GFM-RAG, a novel graph foundation model (GFM) for retrieval augmented generation. GFM-RAG is powered by an innovative graph neural network that reasons over graph structure to capture complex query-knowledge relationships.

Video Anomaly Detection (VAD) aims to locate unusual activities or behaviors within videos. Recently, offline VAD has garnered substantial research attention, which has been invigorated by the progress in large language models (LLMs) and vision-language models (VLMs), offering the potential for a more nuanced understanding of anomalies. However, online VAD has seldom received attention due to real-time constraints and computational intensity.

Plus: Autonomous drones may have killed soldiers for the first time. Imagine tuning in to the opening kickoff of a World Cup match and seeing a player intentionally kick the ball out of bounds. You may question the logic of surrendering possession seconds into a game. If you were Jesse Davis, though, you'd know that this play could be a prime setup to score. Davis is a professor of computer science at KU Leuven in Belgium and head of its Sports Analytics Lab, which has been at the vanguard of a data awakening in soccer. Using AI and data analytics, his team has uncovered hidden tactical patterns and challenged long-held assumptions about how the game should be played.

Combining gradient compression with adaptive optimizers is a highly desirable goal in distributed learning, with potential benefits in both fewer communication rounds and less per-round communication. In spite of preliminary empirical promise, certain major challenges in the convergence analysis of such methods have stayed open: handling compression based approximation of both first and second moments (pre-conditioner) which appear as a ratio; avoiding dependence on the number of parameters, which is extremely large in modern deep models; and providing high-probability guarantees instead of in-expectation, which can hide high variance behavior. In this work, we introduce a family of Sketched Adaptive Distributed Learning (SADL) algorithms which can use suitable unbiased gradient sketching for compression with suitable adaptive optimization algorithms. As our main contribution, we provide theoretical convergence guarantees of SADL algorithms which addresses all of the existing challenges. In particular, our guarantees hold with high probability, picks up only a logarithmic dependence on the number of parameters, and the first and second moment approximation is handled precisely yielding a dependence on the intrinsic dimension of the loss Hessian, which is significantly smaller than the full dimensionality of deep learning models. Empirically, the SADL algorithms are shown to be competitive with and often outperform baselines on both vision and language tasks, in both supervised fine-tuning and training-from-scratch regimes. Further, the SADL algorithms are also competitive with the state-of-the-art communication-efficient distributed learning algorithms based on error feedback.