[Paper Review] A Principle-Driven Adaptive Policy for Group Cognitive Stimulation Dialogue for Elderly with Cognitive Impairment

Jiyue Jiang, Yanyu Chen+8 more|arXiv (Cornell University)|Mar 1, 2026

Digital Mental Health Interventions0 citations

TL;DR

The paper presents GCSD, a principle-driven adaptive policy for multi-party cognitive stimulation dialogue in elderly with cognitive impairment, built on real Cantonese CST data and a Principled Scenario Simulation strategy, achieving superior performance over baselines.

ABSTRACT

Cognitive impairment is becoming a major public health challenge. Cognitive Stimulation Therapy (CST) is an effective intervention for cognitive impairment, but traditional methods are difficult to scale, and existing digital systems struggle with group dialogues and cognitive stimulation principles. While Large Language Models (LLMs) are powerful, their application in this context faces key challenges: cognitive stimulation dialogue paradigms, a lack of therapeutic reasoning, and static-only user modeling. To address these issues, we propose a principle-driven adaptive policy actualized through a Group Cognitive Stimulation Dialogue (GCSD) system. We first construct a dataset with over 500 hours of real-world CST conversations and 10,000+ simulated dialogues generated via our Principle-Guided Scenario Simulation strategy. Our GCSD system then integrates four core modules to overcome LLM limitations: (i) a multi-speaker context controller to resolve role confusion; (ii) dynamic participant cognitive state modeling for personalized interaction; (iii) a cognitive stimulation-focused attention loss to instill cognitive stimulation reasoning; and (iv) a multi-dimensional reward strategy to enhance response value. Experimental results demonstrate that GCSD significantly outperforms baseline models across various evaluation metrics. Future work will focus on long-term clinical validation to bridge the gap between computational performance and clinical efficacy.

Motivation & Objective

• Address the scalability and effectiveness limitations of traditional CST delivered by professionals in group settings.
• Develop a group CST dialogue system capable of multi-party interactions with proper speaker role management.
• Incorporate cognitive stimulation principles into model learning and generation.
• Enable dynamic personalization by modeling participants' cognitive state over time.

Proposed method

• Construct a real Cantonese CST dialogue dataset (~500 hours) with manual transcription and annotation.
• Generate a large simulated dataset using Principle-Guided Scenario Simulation to encode CST principles.
• Propose GCSD with four modules: multi-speaker context controller, dynamic participant cognitive state modeling, cognitive stimulation-focused attention loss, and multi-dimensional reward optimization.
• Pre-train on simulated data, then fine-tune on real data to learn CST structures and language nuances.
• Employ a two-phase optimization: supervised fine-tuning with a cognition-focused attention loss and smoothness regularization, followed by multi-reward policy optimization (MRPO).
• Use a soft-prompt dynamic state mechanism to condition responses, with temporal smoothing to ensure stable personalization.

Experimental results

Research questions

• RQ1 How can a dialogue system identify and maintain correct multi-party speaker roles in group CST conversations?
• RQ2 Can dynamic modeling of each elder’s cognitive state improve personalization and therapeutic relevance in CST dialogues?
• RQ3 Do principle-guided prompts and CST-specific losses improve alignment with CST principles and therapeutic outcomes?
• RQ4 Does a multi-reward policy optimization framework yield higher-quality, more engaging CST dialogues than baseline LLMs?
• RQ5 What is the impact of real+simulated data pre-training on performance and robustness?

Key findings

• GCSD outperforms strong baselines on both automatic and human evaluations in CST dialogue quality.
• BLEU-4 improvements indicate better CST phrasing and multi-party turn-taking learning.
• GCSD achieves higher human relevance scores in multi-party contexts compared to baselines.
• Ablation shows simulated data pre-training, dynamic state modeling, and CST-focused attention loss each contribute to performance.
• Human A/B tests show GCSD’s favorable win rates against several baselines, including GPT-4o in this domain.