Tootfinder

Mentalic Net: Development of RAG-based Conversational AI and Evaluation Framework for Mental Health Support
Anandi Dutta, Shivani Mruthyunjaya, Jessica Saddington, Kazi Sifatul Islam
https://arxiv.org/abs/2509.04456

Mentalic Net: Development of RAG-based Conversational AI and Evaluation Framework for Mental Health Support
The emergence of large language models (LLMs) has unlocked boundless possibilities, along with significant challenges. In response, we developed a mental health support chatbot designed to augment professional healthcare, with a strong emphasis on safe and meaningful application. Our approach involved rigorous evaluation, covering accuracy, empathy, trustworthiness, privacy, and bias. We employed a retrieval-augmented generation (RAG) framework, integrated prompt engineering, and fine-tuned a p…

Designing Gaze Analytics for ELA Instruction: A User-Centered Dashboard with Conversational AI Support
Eduardo Davalos, Yike Zhang, Shruti Jain, Namrata Srivastava, Trieu Truong, Nafees-ul Haque, Tristan Van, Jorge Salas, Sara McFadden, Sun-Joo Cho, Gautam Biswas, Amanda Goodwin
https://arxiv.org/abs/2509.03741

Designing Gaze Analytics for ELA Instruction: A User-Centered Dashboard with Conversational AI Support
Eye-tracking offers rich insights into student cognition and engagement, but remains underutilized in classroom-facing educational technology due to challenges in data interpretation and accessibility. In this paper, we present the iterative design and evaluation of a gaze-based learning analytics dashboard for English Language Arts (ELA), developed through five studies involving teachers and students. Guided by user-centered design and data storytelling principles, we explored how gaze data ca…

Do Large Language Models Need Intent? Revisiting Response Generation Strategies for Service Assistant
Inbal Bolshinsky, Shani Kupiec, Almog Sasson, Yehudit Aperstein, Alexander Apartsin
https://arxiv.org/abs/2509.05006

Do Large Language Models Need Intent? Revisiting Response Generation Strategies for Service Assistant
In the era of conversational AI, generating accurate and contextually appropriate service responses remains a critical challenge. A central question remains: Is explicit intent recognition a prerequisite for generating high-quality service responses, or can models bypass this step and produce effective replies directly? This paper conducts a rigorous comparative study to address this fundamental design dilemma. Leveraging two publicly available service interaction datasets, we benchmark several…

Signal’s Secure Backups sounds like it uses the same mechanism that @… and I designed for completely private (yet company-stored) backups when we were at Matter.
They don’t (and we didn’t) get any of the user’s information, but they’re still able to store the backup data, opaquely, on their servers.

Introducing Signal Secure Backups
In the past, if you broke or lost your phone, your Signal message history was gone. This has been a challenge for people whose most important conversations happen on Signal. Think family photos, sweet messages, important documents, or anything else you don’t want to lose forever. This explains wh...

Recent Trends in Distant Conversational Speech Recognition: A Review of CHiME-7 and 8 DASR Challenges
Samuele Cornell, Christoph Boeddeker, Taejin Park, He Huang, Desh Raj, Matthew Wiesner, Yoshiki Masuyama, Xuankai Chang, Zhong-Qiu Wang, Stefano Squartini, Paola Garcia, Shinji Watanabe
https://arxiv.org/abs/2507.18161

Recent Trends in Distant Conversational Speech Recognition: A Review of CHiME-7 and 8 DASR Challenges
The CHiME-7 and 8 distant speech recognition (DASR) challenges focus on multi-channel, generalizable, joint automatic speech recognition (ASR) and diarization of conversational speech. With participation from 9 teams submitting 32 diverse systems, these challenges have contributed to state-of-the-art research in the field. This paper outlines the challenges' design, evaluation metrics, datasets, and baseline systems while analyzing key trends from participant submissions. From this analysis it …

A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
Olalekan K. Akindele, Bhupesh Kumar Mishra, Kenneth Y. Wertheim
https://arxiv.org/abs/2509.19209

A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
Large Language Models (LLMs) have significantly enhanced conversational Artificial Intelligence(AI) chatbots; however, domain-specific accuracy and the avoidance of factual inconsistencies remain pressing challenges, particularly for large datasets. Designing an effective chatbot with appropriate methods and evaluating its effectiveness is among the challenges in this domain. This study presents a Retrieval Augmented Generation (RAG) chatbot that harnesses a knowledge graph and vector search re…

Challenges and Opportunities for Participatory Design of Conversational Agents for Young People's Wellbeing
Natalia Kucirkova, Alexis Hiniker, Megumi Ishikawa, Sho Tsuji, Aayushi Dangol, Robert Wolfe
https://arxiv.org/abs/2508.14787

Challenges and Opportunities for Participatory Design of Conversational Agents for Young People's Wellbeing
This paper outlines the challenges and opportunities of research on conversational agents with children and young people across four countries, exploring the ways AI technologies can support children's well-being across social and cultural contexts.