The development of AI-assisted Early Intensive Behavioral Intervention (EIBI) for Autism Spectrum Disorder (ASD) is severely constrained by data scarcity. Furthermore, while Applied Behavior Analysis (ABA) serves as the gold standard for clinical intervention, general-purpose Large Language Models (LLMs) struggle to strictly adhere to its standardized procedures, often resulting in interactions that are linguistically fluent but strategically inconsistent. To address these challenges, we introduce ASDAgent, a strategy-aware framework designed to unify high-fidelity intervention dialogue synthesis and clinical decision support. ASDAgent incorporates two specialized components to solve distinct problems: (i) a DoctorAgent equipped with an Observe-Think-Act-Correct (O-T-A-C) reasoning loop, which resolves the issue of strategy collapse in LLMs by making ABA execution explicit and controllable; and (ii) a ChildAgent that utilizes probabilistic behavior modeling to mitigate data homogeneity, simulating diverse and non-deterministic ASD response patterns. Experiments demonstrate that dialogues generated by ASDAgent closely mirror the strategy distribution of human therapists (KL divergence: 0.083). In real autism intervention, ASDAgent achieves nearly 80% strategic consistency with human experts. Moreover, we show that synthetic data produced by ASDAgent effectively distills professional clinical knowledge into small language models (SLMs), significantly enhancing their therapeutic capabilities.

Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios. Retrieval-Augmented Generation (RAG) introduces external knowledge but suffers from hallucinations and latency due to noisy retrievals. Continued pretraining internalizes domain knowledge but is costly and lacks cross-domain flexibility. We attribute this challenge to the long-tail distribution of domain knowledge, which leaves partial yet useful internal knowledge underutilized. We further argue that knowledge acquisition should be progressive, mirroring human learning: first understanding concepts, then applying them to complex reasoning. To address this, we propose Selct2Know (S2K), a cost-effective framework that internalizes domain knowledge through an internal-external knowledge self-selection strategy and selective supervised fine-tuning. We also introduce a structured reasoning data generation pipeline and integrate GRPO to enhance reasoning ability. Experiments on medical, legal, and financial QA benchmarks show that S2K consistently outperforms existing methods and matches domain-pretrained LLMs with significantly lower cost.

Currently, leveraging large language models (LLMs) for autism intervention is a significant yet challenging task, particularly when directly employing LLMs as an intervention doctor. Researchers have mainly focused on using prompt engineering for role play as an intervention doctor and integrating auxiliary elements such as visual stimuli to enhance the sensory experience of the intervention, while neglecting the challenge that LLMs’ inherent dialogue style and intervention strategies do not meet the requirements of clinical dialogue interventions. To fill the gap, we propose a comprehensive framework for training LLMs to conduct dialogue interventions in accordance with the principles of Applied Behavior Analysis (ABA) which is commonly used by clinicians. Specifically, we collected clinical recordings of dialogue interventions for autistic children and constructed the topic dialogue dataset ASD-iLLM-8k. By incorporating the system prompt based on the ABA and ASD-iLLM-8k dataset, we fine-tuned LLMs to develop ASD-iLLM. We also proposed a role-play strategy in which LLMs act as autistic children to comprehensively evaluate the doctor model’s capabilities at the dialogue level. Extensive experiments indicate that ASD-iLLM outperforms existing models in both automatic and human evaluation, with intervention strategies and dialogue style more closely resembling those of clinical intervention doctors. Our dataset, model, and code are available on https://github.com/Shuzhong-Lai/ASD-iLLM.