Tootfinder

Too Big to Prosecute? Why AI’s Use of Search Data Demands Urgent Scrutiny by @HawleyMO https://musictechpolicy.com/2025/07/15/too-big-to-prosecute-why-ais-use-of-search-data-demands-urgent-scrutiny-by-hawleymo/

Too Big to Prosecute? Why AI’s Use of Search Data Demands Urgent Scrutiny by @HawleyMO
Retrieval-Augmented Generation (RAG) gives AI platforms like Google and Microsoft a powerful—and potentially anticompetitive—advantage by repurposing decades of indexed web content to fuel generati…

What is the Best Process Model Representation? A Comparative Analysis for Process Modeling with Large Language Models
Alexis Brissard, Fr\'ed\'eric Cuppens, Amal Zouaq
https://arxiv.org/abs/2507.11356

What is the Best Process Model Representation? A Comparative Analysis for Process Modeling with Large Language Models
Large Language Models (LLMs) are increasingly applied for Process Modeling (PMo) tasks such as Process Model Generation (PMG). To support these tasks, researchers have introduced a variety of Process Model Representations (PMRs) that serve as model abstractions or generation targets. However, these PMRs differ widely in structure, complexity, and usability, and have never been systematically compared. Moreover, recent PMG approaches rely on distinct evaluation strategies and generation techniqu…

GenAI-Enabled Backlog Grooming in Agile Software Projects: An Empirical Study
Kasper Lien Oftebro, Anh Nguyen-Duc, Kai-Kristian Kemell
https://arxiv.org/abs/2507.10753

GenAI-Enabled Backlog Grooming in Agile Software Projects: An Empirical Study
Effective backlog management is critical for ensuring that development teams remain aligned with evolving requirements and stakeholder expectations. However, as product backlogs consistently grow in scale and complexity, they tend to become cluttered with redundant, outdated, or poorly defined tasks, complicating prioritization and decision making processes. This study investigates whether a generative-AI (GenAI) assistant can automate backlog grooming in Agile software projects without sacrifi…

Projected Coupled Diffusion for Test-Time Constrained Joint Generation
Hao Luan, Yi Xian Goh, See-Kiong Ng, Chun Kai Ling
https://arxiv.org/abs/2508.10531 https://

Projected Coupled Diffusion for Test-Time Constrained Joint Generation
Modifications to test-time sampling have emerged as an important extension to diffusion algorithms, with the goal of biasing the generative process to achieve a given objective without having to retrain the entire diffusion model. However, generating jointly correlated samples from multiple pre-trained diffusion models while simultaneously enforcing task-specific constraints without costly retraining has remained challenging. To this end, we propose Projected Coupled Diffusion (PCD), a novel te…

REVA: Supporting LLM-Generated Programming Feedback Validation at Scale Through User Attention-based Adaptation
Xiaohang Tang, Sam Wong, Zicheng He, Yalong Yang, Yan Chen
https://arxiv.org/abs/2507.11470

REVA: Supporting LLM-Generated Programming Feedback Validation at Scale Through User Attention-based Adaptation
This paper introduces REVA, a human-AI system that expedites instructor review of voluminous AI-generated programming feedback by sequencing submissions to minimize cognitive context shifts and propagating instructor-driven revisions across semantically similar instances. REVA introduces a novel approach to human-AI collaboration in educational feedback by adaptively learning from instructors' attention in the review and revision process to continuously improve the feedback validation process. …

A Minimum Distance Estimator Approach for Misspecified Ergodic Processes
Jaroslav I. Borodavka, Sebastian Krumscheid, Grigorios A. Pavliotis
https://arxiv.org/abs/2506.12432

A Minimum Distance Estimator Approach for Misspecified Ergodic Processes
We propose a minimum distance estimator (MDE) for parameter identification in misspecified models characterized by a sequence of ergodic stochastic processes that converge weakly to the model of interest. The data is generated by the sequence of processes, and we are interested in inferring parameters for the limiting processes. We define a general statistical setting for parameter estimation under such model misspecification and prove the robustness of the MDE. Furthermore, we prove the asympt…

Good Morning #Canada
OTD in 1972, construction begins on Canada’s largest single-site #Hydroelectric project at Churchill Falls, Labrador. To commemorate this minor historical event in our country's impressive record of hydroelectric technology and power generation, let's focus on one of the newest projects. The controversial Site C in British Columbia will begin filling its reservoir this summer in preparation for starting operations.
#CanadaIsAwesome
https://youtu.be/gZARb7pLQeo?si=Wo5iEWE9ushqGBaS

Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data
Chandana Cheerla
https://arxiv.org/abs/2507.12425 https://

Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data
Organizations increasingly rely on proprietary enterprise data, including HR records, structured reports, and tabular documents, for critical decision-making. While Large Language Models (LLMs) have strong generative capabilities, they are limited by static pretraining, short context windows, and challenges in processing heterogeneous data formats. Conventional Retrieval-Augmented Generation (RAG) frameworks address some of these gaps but often struggle with structured and semi-structured data.…

Writer Stephanie Wambugu on speaking across generations
#creativity
https://

Writer Stephanie Wambugu on speaking across generations – The Creative Independent
Writer Stephanie Wambugu discusses the conflict of art-world representation, acting as a vessel of human emotion, and resisting trend forecasting.