The paper introduces SIC-Agents, a self‑improving framework designed to enhance simulation for pediatric serious illness communication (SIC) training. It presents two new benchmark suites—PitfallBench and DialogueBench—that assess simulators at both turn‑level and full‑dialogue levels, specifically addressing the unique challenges of multi‑party interactions and parental distress. Experiments demonstrate that SIC‑Agents surpasses static expert prompting, and the authors release the benchmarks for broader research use.
arXiv:2607. 08625v1 Announce Type: new Abstract: Consumer-facing health chatbots powered by large language models (LLMs) are increasingly used for symptom assessment.
arXiv:2407. 03884v4 Announce Type: replace-cross Abstract: Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks.
arXiv:2601. 02871v3 Announce Type: replace Abstract: Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-media contacts for private-channel conversion.
arXiv:2606. 15225v1 Announce Type: cross Abstract: Large-scale learner-task interaction data are crucial for intelligent educational systems but are costly to collect and constrained by privacy and learner engagement.
arXiv:2601.12208v2 Announce Type: replace Abstract: Evaluating conversational systems in multi-turn settings remains a fundamental challenge. Conventional pipelines typically rely on manually defined...
arXiv:2602. 05060v2 Announce Type: replace Abstract: Cybergrooming is an evolving threat to youth, requiring proactive educational interventions.
arXiv:2608.22615v1 Announce Type: new Abstract: Large Language Model (LLM)-based counseling agents can generate fluent and supportive responses, but they often lack the structured, goal-directed prog...
arXiv:2603. 25821v2 Announce Type: replace-cross Abstract: We present Doctorina MedBench, a comprehensive evaluation framework for agent-based medical AI based on the simulation of realistic physician-patient interactions.
arXiv:2603. 25821v3 Announce Type: replace-cross Abstract: We present Doctorina MedBench, an evaluation framework for agent-based medical AI based on the simulation of physician-patient interactions.
arXiv:2606. 30491v1 Announce Type: cross Abstract: Background.
arXiv:2608. 06329v1 Announce Type: cross Abstract: Task-oriented conversational agents are evaluated using curated or automatically generated benchmarks, yet benchmark quality is rarely assessed.
arXiv:2608. 07495v1 Announce Type: cross Abstract: Effective communication during palliative care discussions is a critical clinical skill, yet training clinicians to manage complex patient emotions remains challenging.