ROSCO-Omni: Multimodal LLM-Based Communication Understanding for Non- and Minimally-Speaking Autistic Individuals

Approximately 30% of autistic individuals remain non-or minimally-speaking throughout their lives, yet communicate richly through gestures, vocalizations, facial expressions, and augmentative devices.Interpreting this communication is an inherently multimodal task: caregivers rely on the simultaneous integration of visual cues, auditory signals, and contextual understanding to infer intent.Despite this natural alignment with multimodal large language models (MLLMs), research in this intersection remains narrowly focused on diagnosis rather than communication understanding.We address this gap by reframing the problem around two complementary dimensions: communicative actions (the physical modality) and communicative functions (the pragmatic intent).We analyze the ROSCO dataset, containing 2,903 caregiver-annotated video samples from 27 non-and minimallyspeaking individuals, with multi-label annotations capturing up to three concurrent actions and two functions per sample across 6 action and 6 function classes.We further propose ROSCO-Omni, a teacher-student distillation framework that generates label-guided instruction data from a high-capability teacher MLLM and uses it to finetune a student MLLM for domain-specialized inference.ROSCO-Omni achieves performance comparable to closedsource models, demonstrating that open-source MLLMs can be adapted to understand communication in this underserved population.