Tootfinder

Clustering methods for Categorical Time Series and Sequences : A scoping review
Ottavio Khalifa, Viet-Thi Tran, Alan Balendran, Fran\c{c}ois Petit
https://arxiv.org/abs/2509.07885

Clustering methods for Categorical Time Series and Sequences : A scoping review
Objective: To provide an overview of clustering methods for categorical time series (CTS), a data structure commonly found in epidemiology, sociology, biology, and marketing, and to support method selection in regards to data characteristics. Methods: We searched PubMed, Web of Science, and Google Scholar, from inception up to November 2024 to identify articles that propose and evaluate clustering techniques for CTS. Methods were classified according to three major families -- distance-based,…

RL Is a Hammer and LLMs Are Nails: A Simple Reinforcement Learning Recipe for Strong Prompt Injection
Yuxin Wen, Arman Zharmagambetov, Ivan Evtimov, Narine Kokhlikyan, Tom Goldstein, Kamalika Chaudhuri, Chuan Guo
https://arxiv.org/abs/2510.04885

RL Is a Hammer and LLMs Are Nails: A Simple Reinforcement Learning Recipe for Strong Prompt Injection
Prompt injection poses a serious threat to the reliability and safety of LLM agents. Recent defenses against prompt injection, such as Instruction Hierarchy and SecAlign, have shown notable robustness against static attacks. However, to more thoroughly evaluate the robustness of these defenses, it is arguably necessary to employ strong attacks such as automated red-teaming. To this end, we introduce RL-Hammer, a simple recipe for training attacker models that automatically learn to perform stro…

A Meta-Analysis of LLM Effects on Students across Qualification, Socialisation, and Subjectification
Jiayu Huang (Minhui), Ruoxin Ritter Wang (Minhui), Jen-Hao Liu (Minhui), Boming Xia (Minhui), Yue Huang (Minhui), Ruoxi Sun (Minhui), Jason (Minhui), Xue, Jinan Zou
https://arxiv.org/abs/2509.22725

A Meta-Analysis of LLM Effects on Students across Qualification, Socialisation, and Subjectification
Large language models (LLMs) are increasingly positioned as solutions for education, yet evaluations often reduce their impact to narrow performance metrics. This paper reframes the question by asking "what kind of impact should LLMs have in education?" Drawing on Biesta's tripartite account of good education: qualification, socialisation, and subjectification, we present a meta-analysis of 133 experimental and quasi-experimental studies (k = 188). Overall, the impact of LLMs on student learnin…

A functional tensor model for dynamic multilayer networks with common invariant subspaces and the RKHS estimation
Runshi Tang, Runbing Zheng, Anru R. Zhang, Carey E. Priebe
https://arxiv.org/abs/2509.05221

A functional tensor model for dynamic multilayer networks with common invariant subspaces and the RKHS estimation
Dynamic multilayer networks are frequently used to describe the structure and temporal evolution of multiple relationships among common entities, with applications in fields such as sociology, economics, and neuroscience. However, exploration of analytical methods for these complex data structures remains limited. We propose a functional tensor-based model for dynamic multilayer networks, with the key feature of capturing the shared structure among common vertices across all layers, while simul…