AI co-writing systems challenge long held ideals about agency and ownership in the creative process, thereby hindering widespread adoption. In order to address this, we investigate conceptions of agency and ownership in AI creative co-writing. Drawing on insights from a review of commercial systems, we developed three co-writing systems with identical functionality but distinct interface metaphors: agentic, tool-like, and magical. Through interviews with professional and non-professional writers (n = 18), we explored how these metaphors influenced participants' sense of control and authorship. Our analysis resulted in a taxonomy of agency and ownership subtypes and underscore how tool-like metaphors shift writers' expected points of control while agentic metaphors foreground conceptual contributions. We argue that interface metaphors not only guide expectations of control but also frame conceptions of authorship. We conclude with recommendations for the design of AI co-writing systems, emphasizing how metaphor shapes user experience and creative practice.

Large Language Model (LLMs) can be used to write or modify documents, presenting a challenge for understanding the intent behind their use. For example, benign uses may involve using LLM on a human-written document to improve its grammar or to translate it into another language. However, a document entirely produced by a LLM may be more likely to be used to spread misinformation than simple translation (\eg, from use by malicious actors or simply by hallucinating). Prior works in Machine Generated Text (MGT) detection mostly focus on simply identifying whether a document was human or machine written, ignoring these fine-grained uses. In this paper, we introduce a HiErarchical, length-RObust machine-influenced text detector (HERO), which learns to separate text samples of varying lengths from four primary types: human-written, machine-generated, machine-polished, and machine-translated. HERO accomplishes this by combining predictions from length-specialist models that have been trained with Subcategory Guidance. Specifically, for categories that are easily confused (\eg, different source languages), our Subcategory Guidance module encourages separation of the fine-grained categories, boosting performance. Extensive experiments across five LLMs and six domains demonstrate the benefits of our HERO, outperforming the state-of-the-art by 2.5-3 mAP on average.

The movie industry is associated with an elevated level of risk, which necessitates the use of automated tools to predict box-office revenue and facilitate human decision-making. In this study, we build a sophisticated multimodal neural network that predicts box offices by grounding crowdsourced descriptive keywords of each movie in the visual information of the movie posters, thereby enhancing the learned keyword representations, resulting in a substantial reduction of 14.5% in box-office prediction error. The advanced revenue prediction model enables the analysis of the commercial viability of "copycat movies," or movies with substantial similarity to successful movies released recently. We do so by computing the influence of copycat features in box-office prediction. We find a positive relationship between copycat status and movie revenue. However, this effect diminishes when the number of similar movies and the similarity of their content increase. Overall, our work develops sophisticated deep learning tools for studying the movie industry and provides valuable business insight.

Controlling multiple behavioral attributes in large language models (LLMs) at inference time is a challenging problem due to interference between attributes and the limitations of linear steering methods, which assume additive behavior in activation space and require per-attribute tuning. We introduce K-Steering, a unified and flexible approach that trains a single non-linear multi-label classifier on hidden activations and computes intervention directions via gradients at inference time. This avoids linearity assumptions, removes the need for storing and tuning separate attribute vectors, and allows dynamic composition of behaviors without retraining. To evaluate our method, we propose two new benchmarks, ToneBank and DebateMix, targeting compositional behavioral control. Empirical results across 3 model families, validated by both activation-based classifiers and LLM-based judges, demonstrate that K-Steering outperforms strong baselines in accurately steering multiple behaviors.

The 2025 Ocean Photographer of the Year announced its winners this week. This photo was taken on April 1, 2024, off Point No Point, WA. In Puget Sound, there's a community of people who prefer watching orcas from the land rather than from boats. Land-based whale watchers in Puget Sound can sometimes get lucky, as these wild apex predators occasionally approach the shore, seemingly curious about their human spectators. My friend is one of those land-based whale enthusiasts, and April 1, 2024, was no ordinary day for her.

Breakthroughs, discoveries, and DIY tips sent every weekday. If you own an iPhone 16 or an iPhone 17, you'll find different buttons around the sides of your smartphone. Older iPhones have fewer, depending on the model, but all of these buttons are multitaskers: They come with secondary functions as well as primary ones. For example, did you know you can use either of the volume buttons to snap pictures when you're in the Camera app? This can make it easier to capture photos, compared to trying to hit the circular button on screen.

Your local beetles and chipmunks will thank you for backing away from the rake. Breakthroughs, discoveries, and DIY tips sent every weekday. There's the shift to cooler weather and the opportunity to pull out your favorite hoodie and enjoy the colorful symphony of fall leaves. However, many do not look forward to raking leaves. Believe it or not, you do have a choice when it comes to whether or not to rake leaves.

Has This All Been a Weird Dream? Is A.I. Taking Your Job? For young workers, it might be time to worry. Please enable javascript to get your Slate Plus feeds. If you can't access your feeds, please contact customer support.

A secret Russian-funded network is attempting to disrupt upcoming democratic elections in an eastern European state, the BBC has found. Using an undercover reporter, we discovered the network promised to pay participants if they posted pro-Russian propaganda and fake news undermining Moldova's pro-EU ruling party ahead of the country's 28 September parliamentary ballot. Participants were paid to find supporters of Moldova's pro-Russia opposition to secretly record - and also to carry out a so-called poll. This was done in the name of a non-existent organisation, making it illegal. The results of this selective sampling, an organiser from the network suggested, could lay the groundwork to question the outcome of the election.

Amazon's Zoox robotaxi service offers free driverless rides to Las Vegas Strip locations including Resorts World, AREA15 and Topgolf as part of its public launch.