Tootfinder

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[5/5]:
- General Exploratory Bonus for Optimistic Exploration in RLHF
Wendi Li, Changdae Oh, Sharon Li

Robust Exploratory Stopping under Ambiguity in Reinforcement Learning
Junyan Ye, Hoi Ying Wong, Kyunghyun Park
https://arxiv.org/abs/2510.10260 https://arx…

Robust Exploratory Stopping under Ambiguity in Reinforcement Learning
We propose and analyze a continuous-time robust reinforcement learning framework for optimal stopping problems under ambiguity. In this framework, an agent chooses a stopping rule motivated by two objectives: robust decision-making under ambiguity and learning about the unknown environment. Here, ambiguity refers to considering multiple probability measures dominated by a reference measure, reflecting the agent's awareness that the reference measure representing her learned belief about the env…

LearnLens: An AI-Enhanced Dashboard to Support Teachers in Open-Ended Classrooms
Namrata Srivastava, Shruti Jain, Clayton Cohn, Naveeduddin Mohammed, Umesh Timalsina, Gautam Biswas
https://arxiv.org/abs/2509.10582

LearnLens: An AI-Enhanced Dashboard to Support Teachers in Open-Ended Classrooms
Exploratory learning environments (ELEs), such as simulation-based platforms and open-ended science curricula, promote hands-on exploration and problem-solving but make it difficult for teachers to gain timely insights into students' conceptual understanding. This paper presents LearnLens, a generative AI (GenAI)-enhanced teacher-facing dashboard designed to support problem-based instruction in middle school science. LearnLens processes students' open-ended responses from digital assessments to…

"Pragmatic Tools or Empowering Friends?" Discovering and Co-Designing Personality-Aligned AI Writing Companions
Mengke Wu, Kexin Quan, Weizi Liu, Mike Yao, Jessie Chin
https://arxiv.org/abs/2509.11115

"Pragmatic Tools or Empowering Friends?" Discovering and Co-Designing Personality-Aligned AI Writing Companions
The growing popularity of AI writing assistants presents exciting opportunities to craft tools that cater to diverse user needs. This study explores how personality shapes preferences for AI writing companions and how personalized designs can enhance human-AI teaming. In an exploratory co-design workshop, we worked with 24 writers with different profiles to surface ideas and map the design space for personality-aligned AI writing companions, focusing on functionality, interaction dynamics, and …

Efficient Mining of Low-Utility Sequential Patterns
Jian Zhu, Zhidong Lin, Wensheng Gan, Ruichu Cai, Zhifeng Hao, Philip S. Yu
https://arxiv.org/abs/2510.10243 https://

Efficient Mining of Low-Utility Sequential Patterns
Discovering valuable insights from rich data is a crucial task for exploratory data analysis. Sequential pattern mining (SPM) has found widespread applications across various domains. In recent years, low-utility sequential pattern mining (LUSPM) has shown strong potential in applications such as intrusion detection and genomic sequence analysis. However, existing research in utility-based SPM focuses on high-utility sequential patterns, and the definitions and strategies used in high-utility S…

USA Tariffs Effect: Machine Learning Insights into the Stock Market
Mridul Patel
https://arxiv.org/abs/2510.10877 https://arxiv.org/pdf/2510.10877

USA Tariffs Effect: Machine Learning Insights into the Stock Market
The imposition of tariffs by President Trump during his second term had far-reaching consequences for global markets, including Australia. This study investigates how both the announcement and subsequent implementation of these tariffs, specifically on 02-Apr-2025, affected the Australian stock market, focusing on the S\&P/ASX 200 index over the period from 21-Jan-2025 to 25-Jul-2025. To accurately capture the significance and behavior of market fluctuations, the exploratory data analysis (EDA)…

The bixplot: A variation on the boxplot suited for bimodal data
Camille M. Montalcini, Peter J. Rousseeuw
https://arxiv.org/abs/2510.09276 https://arxiv.or…

The bixplot: A variation on the boxplot suited for bimodal data
Boxplots and related visualization methods are widely used exploratory tools for taking a first look at collections of univariate variables. In this note an extension is provided that is specifically designed to detect and display bimodality and multimodality when the data warrant it. For this purpose a univariate clustering method is constructed that ensures contiguous clusters, meaning that no cluster has members inside another cluster, and such that each cluster contains at least a given num…

Crosslisted article(s) found for q-fin.MF. https://arxiv.org/list/q-fin.MF/new
[1/1]:
- Robust Exploratory Stopping under Ambiguity in Reinforcement Learning
Junyan Ye, Hoi Ying Wong, Kyunghyun Park

PromptMap: Supporting Exploratory Text-to-Image Generation
Yuhan Guo, Xingyou Liu, Xiaoru Yuan, Kai Xu
https://arxiv.org/abs/2510.02814 https://arxiv.org/p…

PromptMap: Supporting Exploratory Text-to-Image Generation
Text-to-image generative models can be tremendously valuable in supporting creative tasks by providing inspirations and enabling quick exploration of different design ideas. However, one common challenge is that users may still not be able to find anything useful after many hours and hundreds of images. Without effective help, users can easily get lost in the vast design space, forgetting what has been tried and what has not. In this work, we first propose the Design-Exploration model to formal…

Exploratory Semantic Reliability Analysis of Wind Turbine Maintenance Logs using Large Language Models
Max Malyi, Jonathan Shek, Andre Biscaya
https://arxiv.org/abs/2509.22366 h…

Exploratory Semantic Reliability Analysis of Wind Turbine Maintenance Logs using Large Language Models
A wealth of operational intelligence is locked within the unstructured free-text of wind turbine maintenance logs, a resource largely inaccessible to traditional quantitative reliability analysis. While machine learning has been applied to this data, existing approaches typically stop at classification, categorising text into predefined labels. This paper addresses the gap in leveraging modern large language models (LLMs) for more complex reasoning tasks. We introduce an exploratory framework t…