In the horticultural world, some vines are especially grabby. As they grow, the woody tendrils can wrap around obstacles with enough force to pull down entire fences and trees. Inspired by vines' twisty tenacity, engineers at MIT and Stanford University have developed a robotic gripper that can snake around and lift a variety of objects, including a glass vase and a watermelon, offering a gentler approach compared to conventional gripper designs. A larger version of the robo-tendrils can also safely lift a human out of bed. The new bot consists of a pressurized box, positioned near the target object, from which long, vine-like tubes inflate and grow, like socks being turned inside out.

Like any other remote perception technology, there are also risks involved with misuse of radar-based perception especially in the context of activity monitoring. Nevertheless, we acquired approvals from Stanford University's IRB The dataset is published under CC BY -NC-ND license. The code is published under Apache License 2.0. The dataset is hosted on a Google Drive space maintained by Stanford University. Doppler snapshots are stored in hdf5 format.

Datasheet Dataset URL Responsibility and statement of license Hosting/maintenance plan Data format Structured metadata UniTox Datasheet Motivation For what purpose was the dataset created? UniTox was created as a unified toxicity dataset across eight types of drug toxicities (cardiotoxicity, liver toxicity, renal toxicity, pulmonary toxicity, hematological toxicity, dermatological toxicity, ototoxicity, and infertility). We generated information across all toxicities for the same set of 2,418 drugs with the same methodology of applying LLMs. For each drug, for each toxicity, we provide an LLM-generated summary of the relevant portions of the drug label, as well as ternary (No/Less/Most) predictions and binary (No/Yes) predictions for that toxicity. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?

Like any other remote perception technology, there are also risks involved with misuse of radar-based perception especially in the context of activity monitoring. Nevertheless, we acquired approvals from Stanford University's IRB The dataset is published under CC BY -NC-ND license. The code is published under Apache License 2.0. The dataset is hosted on a Google Drive space maintained by Stanford University. Doppler snapshots are stored in hdf5 format.

Learning a control policy for a multi-phase, long-horizon task, such as basketball maneuvers, remains challenging for reinforcement learning approaches due to the need for seamless policy composition and transitions between skills. A long-horizon task typically consists of distinct subtasks with well-defined goals, separated by transitional subtasks with unclear goals but critical to the success of the entire task. Existing methods like the mixture of experts and skill chaining struggle with tasks where individual policies do not share significant commonly explored states or lack well-defined initial and terminal states between different phases. In this paper, we introduce a novel policy integration framework to enable the composition of drastically different motor skills in multi-phase long-horizon tasks with ill-defined intermediate states. Based on that, we further introduce a high-level soft router to enable seamless and robust transitions between the subtasks. We evaluate our framework on a set of fundamental basketball skills and challenging transitions. Policies trained by our approach can effectively control the simulated character to interact with the ball and accomplish the long-horizon task specified by real-time user commands, without relying on ball trajectory references.

Evaluating factual accuracy in Large Language Model (LLM)-generated clinical text is a critical barrier to adoption, as expert review is unscalable for the continuous quality assurance these systems require. We address this challenge with two complementary contributions. First, we introduce MedFactEval, a framework for scalable, fact-grounded evaluation where clinicians define high-salience key facts and an "LLM Jury"--a multi-LLM majority vote--assesses their inclusion in generated summaries. Second, we present MedAgentBrief, a model-agnostic, multi-step workflow designed to generate high-quality, factual discharge summaries. To validate our evaluation framework, we established a gold-standard reference using a seven-physician majority vote on clinician-defined key facts from inpatient cases. The MedFactEval LLM Jury achieved almost perfect agreement with this panel (Cohen's kappa=81%), a performance statistically non-inferior to that of a single human expert (kappa=67%, P < 0.001). Our work provides both a robust evaluation framework (MedFactEval) and a high-performing generation workflow (MedAgentBrief), offering a comprehensive approach to advance the responsible deployment of generative AI in clinical workflows.

Economists at Stanford University have found the strongest evidence yet that artificial intelligence is starting to eliminate certain jobs. But the story isn't that simple: While younger workers are being replaced by AI in some industries, more experienced workers are seeing new opportunities emerge. Erik Brynjolfsson, a professor at Stanford University, Ruyu Chen, a research scientist, and Bharat Chandar, a postgraduate student, examined data from ADP, the largest payroll provider in the US, from late 2022, when ChatGPT debuted, to mid-2025. The researchers discovered several strong signals in the data--most notably that the adoption of generative AI coincided with a decrease in job opportunities for younger workers in sectors previously identified as particularly vulnerable to AI-powered automation (think customer service and software development). In these industries, they found a 16 percent decline in employment for workers aged 22 to 25.

Recommender systems for niche and dynamic communities face persistent challenges from data sparsity, cold start users and items, and privacy constraints. Traditional collaborative filtering and content-based approaches underperform in these settings, either requiring invasive user data or failing when preference histories are absent. We present DeepNaniNet, a deep neural recommendation framework that addresses these challenges through an inductive graph-based architecture combining user-item interactions, item-item relations, and rich textual review embeddings derived from BERT. Our design enables cold start recommendations without profile mining, using a novel "content basket" user representation and an autoencoder-based generalization strategy for unseen users. We introduce AnimeULike, a new dataset of 10,000 anime titles and 13,000 users, to evaluate performance in realistic scenarios with high proportions of guest or low-activity users. DeepNaniNet achieves state-of-the-art cold start results on the CiteULike benchmark, matches DropoutNet in user recall without performance degradation for out-of-matrix users, and outperforms Weighted Matrix Factorization (WMF) and DropoutNet on AnimeULike warm start by up to 7x and 1.5x in Recall@100, respectively. Our findings demonstrate that DeepNaniNet delivers high-quality, privacy-preserving recommendations in data-sparse, cold start-heavy environments while effectively integrating heterogeneous content sources.

Transdisciplinary approaches are increasingly essential for addressing grand societal challenges, particularly in complex domains such as Artificial Intelligence (AI), urban planning, and social sciences. However, effectively validating and integrating knowledge across distinct epistemic and ontological perspectives poses significant difficulties. This article proposes a six-dimensional framework for assessing and strengthening transdisciplinary knowledge validity in AI and city studies, based on an extensive analysis of the most cited research (2014--2024). Specifically, the framework classifies research orientations according to ontological, epistemological, methodological, teleological, axiological, and valorization dimensions. Our findings show a predominance of perspectives aligned with critical realism (ontological), positivism (epistemological), analytical methods (methodological), consequentialism (teleological), epistemic values (axiological), and social/economic valorization. Less common stances, such as idealism, mixed methods, and cultural valorization, are also examined for their potential to enrich knowledge production. We highlight how early career researchers and transdisciplinary teams can leverage this framework to reconcile divergent disciplinary viewpoints and promote socially accountable outcomes.

A searchable database of public records concerning use of force and misconduct by California law enforcement officers -- some 1.5 million pages from nearly 700 law enforcement agencies -- is now available to the public. The Police Records Access Project, a database built by UC Berkeley and Stanford University, is being published by the Los Angeles Times, San Francisco Chronicle, KQED and CalMatters. It will vastly expand public access to internal affairs records that show how law enforcement agencies throughout the state handle misconduct allegations and uses of police force that result in death or serious injury. The database currently includes records from nearly 12,000 cases. The database is the product of years of work by a multidisciplinary team of journalists, data scientists, lawyers and civil liberties advocates, led by the Berkeley Institute for Data Science (BIDS), UC Berkeley Journalism's Investigative Reporting Program (IRP) and Stanford University's Big Local News.