Tootfinder

ChatThero: An LLM-Supported Chatbot for Behavior Change and Therapeutic Support in Addiction Recovery
Junda Wang, Zonghai Yao, Zhichao Yang, Lingxi Li, Junhui Qian, Hong Yu
https://arxiv.org/abs/2508.20996

ChatThero: An LLM-Supported Chatbot for Behavior Change and Therapeutic Support in Addiction Recovery
Substance use disorders (SUDs) affect over 36 million people worldwide, yet few receive effective care due to stigma, motivational barriers, and limited personalized support. Although large language models (LLMs) show promise for mental-health assistance, most systems lack tight integration with clinically validated strategies, reducing effectiveness in addiction recovery. We present ChatThero, a multi-agent conversational framework that couples dynamic patient modeling with context-sensitive t…

This year at #CCN2025 we will be showcasing our research on the classification of Mental Workload 🥵 Spatial Effects using Riemannian Manifold.
📅 When: Wednesday, August 13, 1:00 – 4:00 pm
📍 Where: CCN 2025 Conference Venue, de Brug & E-Hall
📋 What: Poster B152

• It leverages advanced mathematical techniques to better understand and classify mental workloads, offerin…

Classification of Mental Workload Spatial Effects using Riemannian Manifold | Next-generation neural computations
This study investigates the use of Riemannian geometry to classify mental workload from an EEG dataset collected in an aeronautical context. The analysis, based on EEG data recorded from 16 participants performing a Simon task, aimed to differentiate low and high workload conditions. Using covariance matrices and a Minimum Distance to Mean (MDM) classifier, the results demonstrate spatial effects of mental workload irrespective of the investigated spectral domain. This demonstrates that spatial…

Empathy-R1: A Chain-of-Empathy and Reinforcement Learning Framework for Long-Form Mental Health Support
Xianrong Yao, Dong She, Chenxu Zhang, Yimeng Zhang, Yueru Sun, Noman Ahmed, Yang Gao, Zhanpeng Jin
https://arxiv.org/abs/2509.14851

Empathy-R1: A Chain-of-Empathy and Reinforcement Learning Framework for Long-Form Mental Health Support
Empathy is critical for effective mental health support, especially when addressing Long Counseling Texts (LCTs). However, existing Large Language Models (LLMs) often generate replies that are semantically fluent but lack the structured reasoning necessary for genuine psychological support, particularly in a Chinese context. To bridge this gap, we introduce Empathy-R1, a novel framework that integrates a Chain-of-Empathy (CoE) reasoning process with Reinforcement Learning (RL) to enhance respon…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/6]:
- A Layered Multi-Expert Framework for Long-Context Mental Health Assessments
Jinwen Tang, Qiming Guo, Wenbo Sun, Yi Shang

Exploring Human-AI Collaboration Using Mental Models of Early Adopters of Multi-Agent Generative AI Tools
Suchismita Naik, Austin L. Toombs, Amanda Snellinger, Scott Saponas, Amanda K. Hall
https://arxiv.org/abs/2510.06224

Exploring Human-AI Collaboration Using Mental Models of Early Adopters of Multi-Agent Generative AI Tools
With recent advancements in multi-agent generative AI (Gen AI), technology organizations like Microsoft are adopting these complex tools, redefining AI agents as active collaborators in complex workflows rather than as passive tools. In this study, we investigated how early adopters and developers conceptualize multi-agent Gen AI tools, focusing on how they understand human-AI collaboration mechanisms, general collaboration dynamics, and transparency in the context of AI tools. We conducted sem…

From Stimuli to Minds: Enhancing Psychological Reasoning in LLMs via Bilateral Reinforcement Learning
Yichao Feng
https://arxiv.org/abs/2508.02458 https://…

From Stimuli to Minds: Enhancing Psychological Reasoning in LLMs via Bilateral Reinforcement Learning
Large Language Models show promise in emotion understanding, social reasoning, and empathy, yet they struggle with psychologically grounded tasks that require inferring implicit mental states in context-rich, ambiguous settings. These limitations arise from the absence of theory-aligned supervision and the difficulty of capturing nuanced mental processes in real-world narratives. To address this gap, we leverage expert-labeled, psychologically rich scenarios and propose a trajectory-aware reinf…

Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory
Shuning Zhang, Rongjun Ma, Ying Ma, Shixuan Li, Yiqun Xu, Xin Yi, Hewu Li
https://arxiv.org/abs/2508.07664

Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory
Large Language Models (LLMs) are increasingly integrating memory functionalities to provide personalized and context-aware interactions. However, user understanding, practices and expectations regarding these memory systems are not yet well understood. This paper presents a thematic analysis of semi-structured interviews with 18 users to explore their mental models of LLM's Retrieval Augmented Generation (RAG)-based memory, current usage practices, perceived benefits and drawbacks, privacy conc…