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#artificial intelligence Preprint Oct 2026

Do Audio LLMs HEAR Pain? Tracing Acoustic Evidence from Speech to Language Reasoning

Large audio language models (LALMs) can process speech directly, but it remains unclear how well they use the non-lexical acoustic information that speech carries. We study this question through pain, using TAME-Pain, which contains pain-neutral Harvard sentences and spoken pain ratings recorded at different self-repor...

Wen-Qi Li, Xiang-Jue Dong, Sulaiman Vesal · 0 citations
#artificial intelligence Preprint Oct 2026

Conditional Accuracy Profiles: Diagnosing LLM Judges across Deployment Conditions

LLM-as-judge is now a standard tool for scalable evaluation, but judge performance is still often summarized by a single accuracy number. This aggregate view hides the deployment conditions under which a judge succeeds or fails. We introduce \textbf{Conditional Accuracy Profiling} (CAP), a post-hoc diagnostic framework...

Wen-Qi Li, Bin Liu, Min-Di Ruan et al. · 0 citations
#artificial intelligence Preprint Oct 2026

A Deterministic Evidence Layer for Vision-Language Autism Screening from Naturalistic Home Video

Autism spectrum disorder (ASD) is diagnosed through specialist observation of a child's social behavior, and access to that expertise is the bottleneck for early identification. Vision-language models (VLMs) describe a child's behavior from video well; the verdict drawn from the description is unstable: at temperature~...

Wen-Qi Li, Min-Di Ruan, Chuan-Bo Hu et al. · 0 citations

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