arXiv — NLP / Computation & Language · · 3 min read

SIC-Agents: Benchmarking and Building an Adaptive Simulator for Pediatric Serious Illness Communication Training

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Computer Science > Computation and Language

arXiv:2608.29481 (cs)
[Submitted on 30 Aug 2026]

Title:SIC-Agents: Benchmarking and Building an Adaptive Simulator for Pediatric Serious Illness Communication Training

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Abstract:Pediatric serious illness communication (SIC) is critically important, yet scalable communication training for clinicians remains limited. Compared with other dialogue simulation settings, pediatric SIC poses additional challenges, including multi-party interactions, response to parental distress and strong dependence on feedback dynamics. Existing LLM-based simulators optimize generic dialogue quality rather than curriculum-contingent behavior required for effective SIC training. In collaboration with educators and pediatric clinicians, we introduce the first benchmark suite and simulation framework tailored to pediatric SIC training. Our benchmarks, PitfallBench and DialogueBench, evaluate simulators both at the turn-level and across full dialogues. We further propose SIC-Agents, a self-improving framework that generates a clinician-editable skill document to guide simulator behavior. Our experiments show that SIC-Agents outperforms static expert prompting. To support future research, we release our benchmarks for parent simulation in pediatric SIC at this https URL
Comments: Accepted to EMNLP 2026
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2608.29481 [cs.CL]
  (or arXiv:2608.29481v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.29481
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zihan Wang [view email]
[v1] Sun, 30 Aug 2026 00:19:44 UTC (533 KB)
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