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An de-identified corpus of 33 cognitive assessment conversations with 8,250 utterances annotated for three speaker roles and 56 dialogue acts is presented, showing that broad conversational intent is easier to recognize than fine-grained communicative function.
Medical visual question answering (Med-VQA) is often assumed to require medical fine-tuning, large models, or complex multi-agent pipelines. We revisit this assumption with \textbf{MedProb}, a lightweight probing framework that predicts multiple-choice Med-VQA answers from frozen VLM representations without free-text g...
It is found that students'detection accuracy improves over time, driven by a shift from relying on linguistic cues to leveraging shared social and contextual signals.
TRACER detects useful failure signals well before the end of the conversation and outperforms heuristic, classical, and single-stream baselines, suggesting that early failure detection can provide a practical warning signal for dialogue systems before the interaction fully breaks down.
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