Adverse drug reaction (ADR) severity prediction is a key task in pharmacovigilance and post-market drug safety surveillance, but is of- ten challenged by noisy and heterogeneous clinical data. We propose a retrieval-augmented learning framework that leverages historical FAERS reports to construct evidence-enriched training instances for ADR severity prediction. Each ADR query, including patient demographics, drug exposure, and indications, is encoded using a fine-tuned BGE-M3 embedding model. We retrieve the top five relevant cases using BM25 lexical search and the top five semantically similar cases using dense vector search, and fuse them via an attention-based aggregation mechanism to produce retrieval-aware representations. These representations are evaluated using machine learning, deep learning, transformer, and fine-tuned large language model approaches for binary severity prediction. Results show that a tree-based ensemble achieves the best performance with an AUC of 82.3%, outperforming the strongest deep learning baseline by 2.3 percentage points and fine-tuned LLMs by 17 percentage points. Overall, the findings demonstrate that retrieval-enhanced data preprocessing can improve representation quality for ADR severity prediction, while the proposed upstream data engineering approach enables classical ensemble methods to remain highly effective for large-scale pharmacovigilance risk prediction.

Temporal question answering (TQA) remains a persistent challenge for large language models (LLMs), particularly in retrieval-augmented generation (RAG) settings where retrieved content may be irrelevant, outdated, or temporally inconsistent. This is especially critical in applications like clinical event ordering, policy tracking, and real-time decision-making, which require reliable temporal reasoning even under noisy or misleading context. To address this challenge, we introduce RASTeR: Robust, Agentic, and Structured, Temporal Reasoning, an agentic prompting framework that separates context evaluation from answer generation. RASTeR first assesses the relevance and temporal coherence of retrieved context, then constructs a structured temporal knowledge graph (TKG) to better facilitate reasoning. When inconsistencies are detected, RASTeR selectively corrects or discards context before generating an answer. Across multiple datasets and LLMs, RASTeR consistently improves robustness: defined here as the model’s ability to generate correct predictions despite suboptimal context. We further validate our approach through a “needle-in-the-haystack” study, in which relevant context is buried among irrelevant distractors. Even with forty distractors, RASTeR achieves 75% accuracy, compared to the runner-up model, which reaches only 62%.