A Proactive Multi-Agent Dialogue Framework for Assessing Social Language Disorder Traits in Autism
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Computer Science > Computation and Language
arXiv:2605.22993 (cs)
[Submitted on 21 May 2026]
Title:A Proactive Multi-Agent Dialogue Framework for Assessing Social Language Disorder Traits in Autism
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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.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.22993 [cs.CL] |
| (or arXiv:2605.22993v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22993
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled A Proactive Multi-Agent Dialogue Framework for Assessing Social Language Disorder Traits in Autism, by Chuanbo Hu and 6 other authors
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