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).

In this paper we present a dilated LSTM with attention mechanism for document-level classification of suicide notes, last statements and depressed notes. We achieve an accuracy of 87.34% compared to competitive baselines of 80.35% (Logistic Model Tree) and 82.27% (Bi-directional LSTM with Attention). Furthermore, we provide an analysis of both the grammatical and thematic content of suicide notes, last statements and depressed notes. We find that the use of personal pronouns, cognitive processes and references to loved ones are most important. Finally, we show through visualisations of attention weights that the Dilated LSTM with attention is able to identify the same distinguishing features across documents as the linguistic analysis.