We present a novel approach for claim verification from tabular data documents. Recent LLM-based approaches either employ complex pretraining/fine-tuning or decompose verification into subtasks, often lacking comprehensive explanations and generalizability. To address these limitations, we propose a Multi-Agentic framework for Claim vErification (MACE) consisting of three specialized agents: Planner, Executor, and Verifier. Instead of elaborate fine-tuning, each agent employs a zero-shot Chain-of-Thought setup to perform its tasks. MACE produces interpretable verification traces, with the Planner generating explicit reasoning strategies, the Executor providing detailed computation steps, and the Verifier validating the logic. Experiments demonstrate that MACE achieves state-of-the-art (SOTA) performance on two datasets and performs on par with the best models on two others, while achieving 80–100% of best performance with substantially smaller models: 27–92B parameters versus 235B. This combination of competitive performance, memory efficiency, and transparent reasoning highlights our framework’s effectiveness.

Discharge summary generation from clinical records is challenging due to long, heterogeneous inputs and the need to preserve clinically relevant information. Although Large Language Models (LLMs) have improved abstractive summarization, their direct application to full patient records is limited by context length and difficulties in aggregating evidence across notes. We present CLARUS, a fully unsupervised framework that models patient records as hypergraphs encoding multi-dimensional relations. A hierarchical hypergraph encoder with depth-aware concept attention strengthens sparse representations, while a variational information bottleneck with Gumbel-Softmax relaxation enables differentiable selection of compact, informative subsets for LLM generation. Experiments on MIMIC-III show that CLARUS outperforms strong unsupervised baselines across lexical, semantic, and clinical metrics, demonstrating the value of higher-order structure and concept hierarchy for faithful multi-document clinical summarization.