Tootfinder

Meta says it will continue to operate and sell the Manus service, and Manus' talent will join Meta to deliver agents across Meta products, including Meta AI (Meta)
https://www.facebook.com/business/news/manus-joins-meta-accelerating-ai-in…

Manus Joins Meta: Accelerating AI Innovation for Businesses
We are excited to announce that Manus is joining Meta to bring a leading agent to billions of people and unlock opportunities for businesses across our products.

Godzilla Day is coming up on November 3, and knowing this in advance gives me time to maybe tidy up the collection to get a good shot by then. We'll see 😂
https://godzilla.com/blogs/news/how-to-celebrate-godzilla-day-2025

How to Celebrate Godzilla Day 2025
More than seven decades after producer Tomoyuki Tanaka, director Ishirō Honda, and special effects master Eiji Tsuburaya first unleashed Godzilla in Japanese theaters on November 3, 1954, the King of the Monsters remains a beloved symbol of imagination and cinematic innovation. Now, after a landmark 70th anniversary ye

A look at Cal State's plans to become an "AI-empowered" university via partnerships with Amazon and a $16.9M deal to provide ChatGPT Edu to students and staff (New York Times)
https://www.nytimes.com/2025/10/26/tec…

Cal State Invited Tech Companies to Remake Learning With A.I.
Spurred by titans like Amazon and OpenAI, California State wants to become the nation’s “largest A.I.-empowered” university.

Bridged Clustering for Representation Learning: Semi-Supervised Sparse Bridging
Patrick Peixuan Ye, Chen Shani, Ellen Vitercik
https://arxiv.org/abs/2510.07182 https://

Bridged Clustering for Representation Learning: Semi-Supervised Sparse Bridging
We introduce Bridged Clustering, a semi-supervised framework to learn predictors from any unpaired input $X$ and output $Y$ dataset. Our method first clusters $X$ and $Y$ independently, then learns a sparse, interpretable bridge between clusters using only a few paired examples. At inference, a new input $x$ is assigned to its nearest input cluster, and the centroid of the linked output cluster is returned as the prediction $\hat{y}$. Unlike traditional SSL, Bridged Clustering explicitly levera…

https://www.nytimes.com/2025/10/06/opinion/politics/how-to-save-the-american-experiment.html?unlocked_article_code=1.rU8.cLIO.j0JwjaQk2D41&smid=url-share

Opinion | Political Violence. Threats to Free Speech. A Disinformation Crisis. We’ve Been Here Before.
To see a way out of our destructive spiral we should look to the innovation of the 1920s.

Agentic generative AI for media content discovery at the national football league
Henry Wang, Sirajus Salekin, Jake Lee, Ross Claytor, Shinan Zhang, Michael Chi
https://arxiv.org/abs/2510.07297

Agentic generative AI for media content discovery at the national football league
Generative AI has unlocked new possibilities in content discovery and management. Through collaboration with the National Football League (NFL), we demonstrate how a generative-AI based workflow enables media researchers and analysts to query relevant historical plays using natural language rather than traditional filter-and-click interfaces. The agentic workflow takes a user query as input, breaks it into elements, and translates them into the underlying database query language. Accuracy and l…

Co-Authoring the Self: A Human-AI Interface for Interest Reflection in Recommenders
Ruixuan Sun, Junyuan Wang, Sanjali Roy, Joseph A. Konstan
https://arxiv.org/abs/2510.08930 ht…

Co-Authoring the Self: A Human-AI Interface for Interest Reflection in Recommenders
Natural language-based user profiles in recommender systems have been explored for their interpretability and potential to help users scrutinize and refine their interests, thereby improving recommendation quality. Building on this foundation, we introduce a human-AI collaborative profile for a movie recommender system that presents editable personalized interest summaries of a user's movie history. Unlike static profiles, this design invites users to directly inspect, modify, and reflect on th…

Paper2Video: Automatic Video Generation from Scientific Papers
Zeyu Zhu, Kevin Qinghong Lin, Mike Zheng Shou
https://arxiv.org/abs/2510.05096 https://arxiv…

Paper2Video: Automatic Video Generation from Scientific Papers
Academic presentation videos have become an essential medium for research communication, yet producing them remains highly labor-intensive, often requiring hours of slide design, recording, and editing for a short 2 to 10 minutes video. Unlike natural video, presentation video generation involves distinctive challenges: inputs from research papers, dense multi-modal information (text, figures, tables), and the need to coordinate multiple aligned channels such as slides, subtitles, speech, and h…

TracE2E: Easily Deployable Middleware for Decentralized Data Traceability
Daniel Pressens\'e, Elisavet Kozyri
https://arxiv.org/abs/2510.08225 https://…

TracE2E: Easily Deployable Middleware for Decentralized Data Traceability
This paper presents TracE2E, a middleware written in Rust, that can provide both data explainability and compliance across multiple nodes. By mediating inputs and outputs of processes, TracE2E records provenance information and enforces data-protection policies (e.g., confidentiality, integrity) that depend on the recorded provenance. Unlike existing approaches that necessitate substantial application modifications, TracE2E is designed for easy integration into existing and future applications …

In-Context Clustering with Large Language Models
Ying Wang, Mengye Ren, Andrew Gordon Wilson
https://arxiv.org/abs/2510.08466 https://arxiv.org/pdf/2510.08…

In-Context Clustering with Large Language Models
We propose In-Context Clustering (ICC), a flexible LLM-based procedure for clustering data from diverse distributions. Unlike traditional clustering algorithms constrained by predefined similarity measures, ICC flexibly captures complex relationships among inputs through an attention mechanism. We show that pretrained LLMs exhibit impressive zero-shot clustering capabilities on text-encoded numeric data, with attention matrices showing salient cluster patterns. Spectral clustering using attenti…