LLM-based AI therapists largely assume cooperative clients, despite real-world therapeutic interactions often involving resistant behaviors: withdrawn, deflecting, denying, or hostile. We propose a resistance-aware AI therapist combining (i) a multi-channel resistance detector that classifies each client turn into one of five types (R0–R4), (ii) a stabilized policy selector that maps a detected resistance to an evidence-based therapeutic strategy, and (iii) a strategy-grounded response generator. Across 800 simulated sessions over ten DSM-5 disorder profiles, the detector reaches 85% accuracy and 85.5% macro F1. Within the simulated evaluation, the adaptive policy achieves resistance-reduction rates of 70–100% across resistance types, whereas each static policy yields zero reduction for at least one type. The adaptive policy also maintains resistance-reduction performance across ten simulated DSM-5 disorder profiles. Our dataset and implementation are fully open-sourced.

Large language models (LLMs) can generate fluent dialogue, but prior works lack situational grounding, dynamic strategy control, and evaluation aligned with clinical standards in motivational interviewing (MI). We introduce StoryMI, a multi-LLM agent framework for controllable MI dialogue generation, where questionnaire-based client profiles are expanded into situational stories that provide narrative context for the dialogue. Therapist and client agents generate MI-coded utterances guided by MI codes selected by the interaction agent, while an interaction agent dynamically coordinates exchanges to control MI strategies during a multi-turn conversation. We propose a two-level evaluation protocol: lexical metrics and MI-specific measures of macro-level counseling strategies, alongside LLM-as-judge and human expert assessments. We construct a dataset of 6K simulated MI dialogues grounded in 1K questionnaire-story pairs, covering 12 MI codes and 13 symptom domains, and benchmark six open- and closed-source LLMs. Our results show that situational grounding and macro-level control can improve MI adherence and clinical plausibility, demonstrating the effectiveness of a structured multi-agent workflow for psychotherapy dialogue generation. We provide code and data for reproducibility.

This work investigates the ability of large language models (LLMs) to generate mathematical equations from scientific texts. Prior work faces challenges in unstructured grounding, multi-equation dependency, and human-aligned evaluation. To address this, we construct a dataset of AI research papers, pairing contextual passages with ground-truth equations and variable descriptions. We develop an explainable equation generation workflow and evaluate it across diverse open- and closed-source LLMs. Our evaluation protocol combines automatic metrics, LLM-based rubrics, and human judgments to assess accuracy, explainability, and human-LLM alignment. Results show that LLMs achieve moderate performance on lexical and syntactic similarity, but struggle with semantic accuracy. LLM-based evaluations show limited alignment with human judgments, highlighting challenges in assessing equation quality. These findings provide insights for improving equation generation models and developing more reliable evaluation methods for scientific creativity. We provide code and data for reproducibility.