Learning how the world works is central to building AI agents that can adapt to complex environments. Traditional world models based on deep learning demand vast amounts of training data, and do not flexibly update their knowledge from sparse observations. Recent advances in program synthesis using Large Language Models (LLMs) give an alternate approach which learns world models represented as source code, supporting strong generalization from little data. To date, application of program-structured world models remains limited to natural language and grid-world domains. We introduce a novel program synthesis method for effectively modeling complex, non-gridworld domains by representing a world model as an exponentially-weighted product of programmatic experts (PoE-World) synthesized by LLMs. We show that this approach can learn complex, stochastic world models from just a few observations. We evaluate the learned world models by embedding them in a model-based planning agent, demonstrating efficient performance and generalization to unseen levels on Atari's Pong and Montezuma's Revenge.

This position paper argues that real-time generative AI has the potential to become the next wave of addictive digital media, creating a new class of digital content akin to "digital heroin" with severe implications for mental health and youth development. By shortening the content-generation feedback loop to mere seconds, these advanced models will soon be able to hyper-personalize outputs on the fly. When paired with misaligned incentives (e.g., maximizing user engagement), this will fuel unprecedented compulsive consumption patterns with far-reaching consequences for mental health, cognitive development, and social stability. Drawing on interdisciplinary research, from clinical observations of social media addiction to neuroscientific studies of dopamine-driven feedback, we illustrate how real-time tailored content generation may erode user autonomy, foment emotional distress, and disproportionately endanger vulnerable groups, such as adolescents. Due to the rapid advancement of generative AI and its potential to induce severe addictionlike effects, we call for strong government oversight akin to existing controls on addictive substances, particularly for minors. We further urge the machine learning community to act proactively by establishing robust design guidelines, collaborating with public health experts, and supporting targeted policy measures to ensure responsible and ethical deployment, rather than paving the way for another wave of unregulated digital dependence.

From media platforms to chatbots, algorithms shape how people interact, learn, and discover information. Such interactions between users and an algorithm often unfold over multiple steps, during which strategic users can guide the algorithm to better align with their true interests by selectively engaging with content. However, users frequently exhibit inconsistent preferences: they may spend considerable time on content that offers little long-term value, inadvertently signaling that such content is desirable. Focusing on the user side, this raises a key question: what does it take for such users to align the algorithm with their true interests? To investigate these dynamics, we model the user's decision process as split between a rational "system 2" that decides whether to engage and an impulsive "system 1" that determines how long engagement lasts.

Current benchmarks for evaluating large language models (LLMs) in social media moderation completely overlook a serious threat: covert advertisements, which disguise themselves as regular posts to deceive and mislead consumers into making purchases, leading to significant ethical and legal concerns. In this paper, we present the CHASM, a first-of-its-kind dataset designed to evaluate the capability of Multimodal Large Language Models (MLLMs) in detecting covert advertisements on social media. CHASM3 is a high-quality, anonymized, manually curated dataset consisting of 4,992 instances, based on real-world scenarios from the Chinese social media platform Rednote. The dataset was collected and annotated under strict privacy protection and quality control protocols. It includes many product experience sharing posts that closely resemble covert advertisements, making the dataset particularly challenging.

Novel view synthesis is an important problem with many applications, including AR/VR, gaming, and robotic simulations. With the recent rapid development of Neural Radiance Fields (NeRFs) and 3DGaussian Splatting (3DGS) methods, it is becoming difficult to keep track of the current state of the art (SoTA) due to methods using different evaluation protocols, codebases being difficult to install and use, and methods not generalizing well to novel 3D scenes. In our experiments, we show that even tiny differences in the evaluation protocols of various methods can artificially boost the performance of these methods. This raises questions about the validity of quantitative comparisons performed in the literature. To address these questions, we propose NerfBaselines, an evaluation framework which provides consistent benchmarking tools, ensures reproducibility, and simplifies the installation and use of various methods. We validate our implementation experimentally by reproducing the numbers reported in the original papers. For improved accessibility, we release a web platform that compares commonly used methods on standard benchmarks. We strongly believe NerfBaselines is a valuable contribution to the community as it ensures that quantitative results are comparable and thus truly measure progress in the field of novel view synthesis.

Plus, a timely soccer-related management sim that doesn't hit the back of the net. Welcome to our latest roundup of what's going on in the indie game space. As ever, we've got some new releases and news of upcoming games to share with you. However, it's Steam Next Fest time, so there are literally thousands of fresh game demos to jump into. One of the main aims of Steam Next Fest is to help players discover interesting games.

Unlike traditional benchmarks for scientific literature understanding and synthesis, SciArena engages the research community directly, following the Chatbot Arena evaluation approach of community voting on model comparisons. By leveraging collective intelligence, SciArena offers a community-driven evaluation of model performance on open-ended scientific tasks that demand literature-grounded, long-form responses. The platform currently supports 47 foundation models and has collected over 20,000 votes from human researchers across diverse scientific domains. Our analysis of the data collected so far confirms its high quality. We discuss the results and insights based on the model ranking leaderboard. To further promote research in building modelbased automated evaluation systems for literature tasks, we release SciArena-Eval, a meta-evaluation benchmark based on collected preference data. It measures the accuracy of models in judging answer quality by comparing their pairwise assessments with human votes. Our experiments highlight the benchmark's challenges and emphasize the need for more reliable automated evaluation methods.

A.1 Coordinate Systems and Transformation To achieve spatial synchronization between different sensors, vehicle-vehicle-UAV collaboration requires using sensor parameter information to perform coordinate system transformations. The relationships between the coordinate systems are illustrated in Fig. S 1. Figure 1: Relationship between coordinate systems. The pixel coordinate system refers to a two-dimensional coordinate system defined on the image plane, typically represented as (u,v), with units in pixels. In this system, the origin is located at the top-left corner of the image, the u-axis points to the right along the horizontal direction, and the v-axis points downward along the vertical direction. This coordinate system is used to describe the position of points on the two-dimensional image captured by the camera.

By sharing information across multiple agents, collaborative perception helps autonomous vehicles mitigate occlusions and improve overall perception accuracy. While most previous work focus on vehicle-to-vehicle and vehicle-to-infrastructure collaboration, with limited attention to aerial perspectives provided by UAVs, which uniquely offer dynamic, top-down views to alleviate occlusions and monitor large-scale interactive environments. A major reason for this is the lack of highquality datasets for aerial-ground collaborative scenarios. To bridge this gap, we present AGC-Drive, the first large-scale real-world dataset for Aerial-Ground Cooperative 3D perception. The data collection platform consists of two vehicles, each equipped with five cameras and one LiDAR sensor, and one UAV carrying a forward-facing camera and a LiDAR sensor, enabling comprehensive multi-view and multi-agent perception.

Comparison of RAPID, LEAP, and Allegro infinger thickness, MCP joint configurations, and dexterity, illustrated with representative finger poses.