@inproceedings{antony-etal-2026-agentic,
    title = "Agentic Pipelines Meet Retrieval-Augmented {ICL}: A Zero-Training Approach to Mental Health Modeling",
    author = "Antony, Anson  and
      Kumar, Gautam  and
      Schoene, Annika Marie",
    editor = "Zirikly, Aya  and
      Bar, Kfir  and
      MacAvaney, Sean  and
      Ireland, Molly  and
      Ophir, Yaakov  and
      Atzil-Slonim, Dana  and
      Varadarajan, Vasudha  and
      Bedrick, Steven  and
      Desmet, Bart",
    booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.clpsych-1.34/",
    pages = "435--440",
    ISBN = "979-8-89176-421-7",
    abstract = "This paper describes a system for the CLPsych 2026 shared task that uses retrieval-augmented in-context learning with frozen LLMs and no fine-tuning. The core contribution is a five-agent agentic pipeline for Task 3.1 sequence summarisation: two rule-based agents detect change type (Switch/Escalation) and direction (improvement/deterioration), an LLM-based DynamicsExtractor produces structured ABCD analysis, a SummaryWriter composes prose grounded in retrieved gold exemplars, and a Validator enforces structural constraints. This pipeline is iteratively refined across three submissions via NLI-based candidate reranking and per-sentence contradiction reduction. For Tasks 1.1 and 1.2, a single LLM call combines static and RAG-retrieved examples; for Task 2, an auto-tuned prompt detects moments of change. The system ranked 1st on Task 1.2 (RMSE 0.917) and Task 3.1 (score rank average 4.00), 3rd on Task 1.1 (F1 0.420), and 8th on Task 2 (F1 0.466)."
}