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arXiv · 2608.12329

AnchorSIPS: A Synthetic Dataset and Evaluation Resource for Evidence-Supported Psychosis-Risk Symptom Measurement

Guilherme C. Oliveira·Stephanie Fong·Zimu Wang·Clarice Lee·Xiangyu Zhao·Duy Khoa Pham·Duong Nhu·Yiwen Jiang·Jiahe Liu·Zhongxing Xu·Dwarikanath Mahapatra·Dominic Dwyer·Zongyuan Ge
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Abstract

Progress on AI for psychosis-risk assessment is limited by a data-access bottleneck. Real clinical interviews are difficult to share because of privacy, governance, and consent constraints. We present AnchorSIPS, a synthetic dataset of 10K structured psychosis-risk interviews with transcript-grounded measurement targets. Each interview is modeled on Mini-SIPS, a clinician-administered psychosis-risk interview. It captures history, 24 symptom questions, follow-up evidence for items the patient affirms, decisions about delusion-like symptoms (unusual beliefs), hallucination-like symptoms (unusual perceptions), and disorganized communication, exclusion of clear psychotic-level symptoms ("frank psychosis"), and a final attenuated psychosis syndrome (APS) diagnosis, a high-risk state of milder or early psychotic symptoms. The APS diagnosis is not a standalone label. It depends on earlier endorsements, supporting follow-up details, symptom-class decisions, and the frank-psychosis check. Every intermediate decision is anchored to its supporting transcript turns. AnchorSIPS is generated by a plan-then-realize pipeline. A hidden case sheet specifies the patient's clinical state, a deterministic planner fixes the interview structure, and an LLM realizes only the patient utterances under validation and bounded repair. Fixing labels and structure before generation avoids the inter-turn inconsistencies typical of multi-turn LLM dialogue. Across seven LLM baselines, models recover coarse decisions but fail to extract follow-up details or cite supporting transcript turns, so final-label performance overstates interview competence. AnchorSIPS is intended for research on evidence extraction, transcript-grounded measurement, and uncertainty under partial disclosure.

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Guilherme C. Oliveira, Stephanie Fong, Zimu Wang, Clarice Lee, Xiangyu Zhao, Duy Khoa Pham, Duong Nhu, Yiwen Jiang, Jiahe Liu, Zhongxing Xu, Dwarikanath Mahapatra, Dominic Dwyer, Zongyuan Ge. 2026-06-01. AnchorSIPS: A Synthetic Dataset and Evaluation Resource for Evidence-Supported Psychosis-Risk Symptom Measurement. https://arxiv.org/abs/2608.12329

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