arXiv:2510.03223v3 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) tend to generate a long reasoning chain when solving complex tasks. However, as the reasoning chain extends, cri...
By Hongxiang Zhang, Yuan Tian, Tianyi Zhang
The paper introduces PsyLLM, a large language model specifically designed for mental health counseling that integrates diagnostic reasoning and multiple therapeutic frameworks. It employs an automated data synthesis pipeline that transforms Reddit mental‑health posts into multi‑turn counseling dialogues, guided by DSM/ICD standards and therapies such as CBT, ACT, and psychodynamic approaches. A new benchmark and evaluation protocol show that PsyLLM outperforms baseline models, with results validated by professional counseling experts.
By He Hu, Juzheng Si, Qianning Wang, Fei Ma, Fuji Ren, Sicheng Zhao, Yucheng Zhou, Laizhong Cui
The paper presents a taxonomy of 28 cognitive elements derived from cognitive science to analyze reasoning in large language models (LLMs). Using a fine‑grained evaluation framework, the authors compare 192,000 model traces with 1,346 human think‑aloud traces, finding that while models deploy similar cognitive elements, they differ in usage patterns. The study introduces test‑time reasoning guidance that improves model accuracy on ill‑structured problems by 9.7 percentage points and highlights a research gap in self‑awareness, advocating for a shared vocabulary to diagnose failures and guide robust model development.
By Shuyue Stella Li, Priyanka Kargupta, Haocheng Wang, Jinu Lee, Dean Light, Shan Chen, Orevaoghene Ahia, Thomas L. Griffiths, Max Kleiman-Weiner, Jiawei Han, Asli Celikyilmaz, Yulia Tsvetkov
arXiv:2609.22700v1 Announce Type: new
Abstract: Step-level reasoning evaluators are commonly based on autoregressive language models, whose causal attention restricts each step representation to the...
By Yiming Feng, Naihao Deng, Yulong Chen, Rada Mihalcea
MindEval2 is a new evaluation framework for AI chatbots in mental health support, comprising Mental Health Quality Rubrics (MHQR) and the MindSim patient simulation harness. MHQR, developed with PhD-level psychologists, provides explicit, actionable criteria for assessing response quality, while MindSim offers realistic, longitudinal patient interactions with a memory component. The study evaluated 18 models, finding that although surface-level quality has improved, guidance calibration remains the main failure across all models.
By John Mendon\c{c}a, Daniel Tschernutter, Jos\'e Pombal, Areti Vassilopoulos, Catarina Botelho, Ignacio Sarasua, Maya D'Eon, Nuno Guerreiro, Ricardo Rei
The paper explores how Large Language Models (LLMs) such as GPT-4o-mini, Gemini-1.5-pro, and LLaMA can be used to automatically simplify complex medical texts into plain language, a process known as Plain Language Adaptation (PLA). It evaluates different prompting strategies, fine‑tunes models with QLoRA, and incorporates Mixture‑of‑Agents (MoA) techniques to improve adaptability and robustness. The results show that LLM‑driven PLA effectively makes healthcare information more comprehensible while preserving essential content.
By Ting-Wei Chang, Hen-Hsen Huang, Hsin-Hsi Chen