Recommenders are significantly shaping online information consumption. While effective at personalizing content, these systems increasingly face criticism for propagating irrelevant, unwanted, and even harmful recommendations. Such content degrades user satisfaction and contributes to significant societal issues, including misinformation, radicalization, and erosion of user trust. Although platforms offer mechanisms to mitigate exposure to undesired content, these mechanisms are often insufficiently effective and slow to adapt to users' feedback. This paper introduces an intuitive, model-agnostic, and distribution-free method that uses conformal risk control to provably bound unwanted content in personalized recommendations by leveraging simple binary feedback on items. We also address a limitation of traditional conformal risk control approaches, i.e., the fact that the recommender can provide a smaller set of recommended items, by leveraging implicit feedback on consumed items to expand the recommendation set while ensuring robust risk mitigation. Our experimental evaluation on data coming from a popular online video-sharing platform demonstrates that our approach ensures an effective and controllable reduction of unwanted recommendations with minimal effort. The source code is available here: https://github.com/geektoni/mitigating-harm-recsys.

Large language models (LLMs) turn writing into a live exchange between humans and software. We characterize this new medium as a discursive network that treats people and LLMs as equal nodes and tracks how their statements circulate. We define the generation of erroneous information as invalidation (any factual, logical, or structural breach) and show it follows four hazards: drift from truth, self-repair, fresh fabrication, and external detection. We develop a general mathematical model of discursive networks that shows that a network governed only by drift and self-repair stabilizes at a modest error rate. Giving each false claim even a small chance of peer review shifts the system to a truth-dominant state. We operationalize peer review with the open-source Flaws-of-Others (FOO) algorithm: a configurable loop in which any set of agents critique one another while a harmonizer merges their verdicts. We identify an ethical transgression, epithesis, that occurs when humans fail to engage in the discursive network. The takeaway is practical and cultural: reliability in this new medium comes not from perfecting single models but from connecting imperfect ones into networks that enforce mutual accountability.

Effectively engaging the public with science is vital for fostering trust and understanding in our scientific community. Yet, with an ever-growing volume of information, science communicators struggle to anticipate how audiences will perceive and interact with scientific news. In this paper, we introduce a computational framework that models public perception across twelve dimensions, such as newsworthiness, importance, and surprisingness. Using this framework, we create a large-scale science news perception dataset with 10,489 annotations from 2,101 participants from diverse US and UK populations, providing valuable insights into public responses to scientific information across domains. We further develop NLP models that predict public perception scores with a strong performance. Leveraging the dataset and model, we examine public perception of science from two perspectives: (1) Perception as an outcome: What factors affect the public perception of scientific information? (2) Perception as a predictor: Can we use the estimated perceptions to predict public engagement with science? We find that individuals' frequency of science news consumption is the driver of perception, whereas demographic factors exert minimal influence. More importantly, through a large-scale analysis and carefully designed natural experiment on Reddit, we demonstrate that the estimated public perception of scientific information has direct connections with the final engagement pattern. Posts with more positive perception scores receive significantly more comments and upvotes, which is consistent across different scientific information and for the same science, but are framed differently. Overall, this research underscores the importance of nuanced perception modeling in science communication, offering new pathways to predict public interest and engagement with scientific content.

Recent studies have explored graph-based approaches to retrieval-augmented generation, leveraging structured or semi-structured information -- such as entities and their relations extracted from documents -- to enhance retrieval. However, these methods are typically designed to address specific tasks, such as multi-hop question answering and query-focused summarisation, and therefore, there is limited evidence of their general applicability across broader datasets. In this paper, we aim to adapt a state-of-the-art graph-based RAG solution: $\text{GeAR}$ and explore its performance and limitations on the SIGIR 2025 LiveRAG Challenge.

In the human-bot symbiotic information ecosystem, social bots play key roles in spreading and correcting disinformation. Understanding their influence is essential for risk control and better governance. However, current studies often rely on simplistic user and network modeling, overlook the dynamic behavior of bots, and lack quantitative evaluation of correction strategies. To fill these gaps, we propose MADD, a Multi Agent based framework for Disinformation Dissemination. MADD constructs a more realistic propagation network by integrating the Barabasi Albert Model for scale free topology and the Stochastic Block Model for community structures, while designing node attributes based on real world user data. Furthermore, MADD incorporates both malicious and legitimate bots, with their controlled dynamic participation allows for quantitative analysis of correction strategies. We evaluate MADD using individual and group level metrics. We experimentally verify the real world consistency of MADD user attributes and network structure, and we simulate the dissemination of six disinformation topics, demonstrating the differential effects of fact based and narrative based correction strategies.

HHS Secretary told members of Congress on Tuesday that wearables are "a way of people can take control over their own health." Have you ever noticed how a spilled cup of coffee leaves behind a telltale brown ring? While those stains might be annoying, the science behind them, known as the coffee ring effect, has sparked innovations in health technology. UC Berkeley researchers recently turned this everyday phenomenon into a breakthrough medical test, making rapid and reliable disease detection as easy as brewing your morning coffee. Curious how a simple coffee stain could inspire cutting-edge diagnostics and revolutionize at-home testing?

We write a lot about robots here at New Scientist – the latest cutting-edge developments, the newest technology. But we also have a great deal of fondness for them in fiction, whether that's the super cute likes of WALL-E and BB-8, or the darker side of the robotic family, from the Terminator to Ava from Ex Machina. Last month, Sierra Greer's novel about the rebellion of a robot designed for intimacy, Annie Bot, won this year's Arthur C Clarke award, the UK's top prize for science fiction. It was described by judges as "a tightly-focused first person account of a robot designed to be the perfect companion who struggles to become free". Greer's win felt like the right moment to ask New Scientist staff to nominate their own favourite fictional robotic beings, from page or screen. After a bit of quibbling about what constitutes a robot, and a lot of people plumping for various Star Wars droids and Futurama creations, here, in no particular order, they are.

Wegmans is testing AI-powered Caper Carts at four New York locations, allowing shoppers to track spending in real time and skip checkout lines with automatic item detection technology.

At WIRED, we're obsessed with how the world is transforming--and lately, there's been a lot to obsess over. From the breakneck pace of AI research to the tectonic transformation playing out across the US federal government, WIRED's journalists, producers, and editors are committed to reporting from the front lines of these changes and bringing all of you along for the ride. Our goal is to wake up every day and unearth what we describe as "Story Zero": the story before anybody even knows there's a story to tell. We endeavor to do that work in a way that's conversational and accessible, fearless and definitive, and ultimately helps you understand what's changing, why, and how it'll affect your present and your future. I'm incredibly proud that our work this year has often achieved the lofty goals we set for ourselves: WIRED journalists have produced groundbreaking reporting on DOGE's disruption of federal agencies, unearthed ambiguities in the Jeffery Epstein video, delivered a constant drumbeat of clear-eyed coverage on AI's real-world impact (and the AI industry's outrageous talent wars), and found the time to execute on narrative stories that run the gamut, from an AI-inflected murder cult to the quantum apocalypse right around the corner.

A growing number of fire departments across the country are turning to artificial intelligence to help detect and respond to wildfires more quickly. A major breakthrough in artificial intelligence and high-performance computing is on the way, and it's coming from Georgia Tech. Backed by a 20 million investment from the National Science Foundation (NSF), the university is building a supercomputer named Nexus. It's expected go online in spring 2026. Sign up for my FREE CyberGuy Report Get my best tech tips, urgent security alerts and exclusive deals delivered straight to your inbox.