A Proactive Multi-Agent Dialogue Framework for Assessing Social Language Disorder Traits in Autism

Researchers have developed TPA (Think, Plan, Ask), a proactive multi-agent dialogue framework that enables large language models to systematically select questioning strategies to surface latent social language disorder traits in autism. In evaluations on 484 episodes from 35 patients, TPA achieved 82.1% trait coverage, outperforming six baseline methods and surpassing the 65.5% coverage of real clinical dialogues conducted by trained clinicians. The framework demonstrates that proactive questioning strategy selection can substantially improve the efficiency of automated SLD trait assessment, with direct implications for scalable AI-assisted clinical screening.

arXiv:2605.22993v1 Announce Type: new Abstract: Characteristic linguistic behaviors associated with Social Language Disorder SLD in autism spectrum disorder, including echoic repetition, pronoun displacement, and stereotyped media quoting, are largely absent from spontaneous conversation and only emerge under specific conversational conditions. In structured clinical assessments, this latency means that questioning strategy selection is a critical yet underappreciated determinant of how much diagnostic information a conversation yields. Whether large language models LLMs can be guided to proactively select questioning strategies that systematically surface these latent traits remains largely unexplored. Here we present TPA Think, Plan, Ask , a proactive multi-agent dialogue framework applied to the language assessment component of the Autism Diagnostic Observation Schedule Module 4 ADOS-2 , in which a doctor agent explicitly reasons about which traits remain unobserved before selecting a clinically grounded strategy and generating a targeted question. A patient agent grounded in real ADOS-2 clinical data enables reproducible evaluation without real patient participation, validated across three independent experiments confirming adequate fidelity to real patient language. Evaluated on 484 episodes from 35 patients, TPA outperforms six competitive dialogue planning baselines across all primary metrics, achieving 82.1% SLD trait coverage, 16.6% higher than automated replay of real clinical dialogues conducted by trained clinicians 65.5% , with substantially greater per-turn diagnostic efficiency AUCC: 0.628 vs. 0.458, absolute gain +0.170 . These results demonstrate that proactive questioning strategy selection substantially improves the efficiency of automated SLD trait assessment, with direct implications for scalable AI-assisted clinical screening.