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Intelligence Artificielle

Evaluation of Phonetic Encoding Algorithms on Transcription Datasets

arXiv:2609.04391v1 Announce Type: new Abstract: In this work, a novel evaluation scheme built on a generalized variant of the Rand Index measure, namely, the H\"ullermeier-Rifqi Index, is proposed in order to assess how well phonetic encoding algorithms conform to word-based transcriptions in IPA (International Phonetic Alphabet) notation. For this objective, the discordance…

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Intelligence Artificielle

You Really Didn't Get That? Benchmarking Social Pragmatic Inference for Indirect and Playful Chinese Online Comments

arXiv:2609.04384v1 Announce Type: new Abstract: Chinese online comments often convey social meaning through indirect and playful language that is hard to interpret without context. Existing evaluations largely organize items around predefined phenomena or controlled pragmatic categories, leaving open whether models can distinguish plausible readings of what a naturally…

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Intelligence Artificielle

VERGE: Verification-Enhanced Refinement for Grounded Extraction of Early-Onset Colorectal Cancer Symptoms in Clinical Notes

arXiv:2609.04366v1 Announce Type: new Abstract: Early-onset colorectal cancer is increasing among younger adults, yet red-flag symptoms in this age group have no evidence-based guidelines for follow-up testing, and structured encounter data do not capture the detail needed to support early detection and inform follow-up, including symptom duration, context, and fam- ily…

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Intelligence Artificielle

Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselors

arXiv:2609.04350v1 Announce Type: new Abstract: How do people learn to become better conversationalists? This question is especially important in the context of mental-health counseling, where conversational skills are essential, yet volunteer counselors often have limited access to supervision and structured feedback. Understanding how counselors develop their ability to…

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Intelligence Artificielle

MedProb: Probing Internal Representations of Vision-Language Models for Medical Question Answering

arXiv:2609.04336v1 Announce Type: new Abstract: 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 generation.…

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Intelligence Artificielle

Evidence Integration in Large Language Models

arXiv:2609.04290v1 Announce Type: new Abstract: Despite increasing reliance on LLMs that reason with external evidence supplied by tools, retrieval-augmented generation, other agents, and users, how LLMs integrate such evidence into decisions they have already begun to form remains largely unclear. We present a distributional theory in which evidence shifts the receiver's…

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