Large Multimodal Models (LMMs) achieve state-of-the-art performance in high-stakes domains like healthcare, yet their reasoning remains opaque. Attention- and saliency-based methods often fail to faithfully represent the model’s decision process, particularly when integrating heterogeneous modalities. We introduce Tree-of-Evidence (ToE), an inference-time search algorithm that frames interpretability as a discrete optimization problem. Rather than relying on soft attention weights, ToE employs lightweight Evidence Bottlenecks that score coarse units of data (e.g., vital-sign windows, report chunks) and performs a beam search to identify the compact evidence set required to reproduce the model’s prediction. We evaluate ToE across six tasks spanning three datasets and two domains, including clinical prediction on MIMIC-IV, cross-center validation on eICU, and non-clinical fault detection on LEMMA-RCA. ToE retains over 98% of full-model AUROC with as few as five evidence units, achieves higher decision agreement and lower fidelity error than LIME, SHAP, saliency, and concept-bottleneck baselines under sparse budgets, and outperforms LLMs up to 70B parameters. ToE therefore provides a practical mechanism for auditing multimodal models.

Clinical language and vision-language models increasingly generate diagnostic and treatment rationales, but those claims remain difficult to audit. Existing benchmarks usually score final answers over fully revealed records, without testing whether each claim is grounded in valid patient evidence or whether models can seek missing information efficiently. We introduce Provena-Med, a benchmark for evaluating provenance-faithful multimodal clinical reasoning over linked EHR tables, clinical notes, chest radiographs, and guideline rules. Models must produce a staged artifact comprising a problem list, differential, workup, therapy, and monitoring, in which each claim cites typed evidence units and may abstain when information is insufficient. Provena-Med supports both full-information evaluation and an interactive evidence-seeking track. We further propose MM-AIS, which measures citation validity, objective support under evidence-removal tests, salient-evidence recall, and, for open-weight models, sensitivity to cited evidence. Across ten open-weight models and five cohorts, the best attribution yield is only 0.42, citation hallucination is common, and diagnostic accuracy, safety, and provenance do not reliably co-vary. Provena-Med provides an auditable testbed for evaluating grounded clinical reasoning beyond final-answer accuracy.

Large Language Models (LLMs) have demonstrated remarkable capabilities on general text; however, their proficiency in specialized scientific domains that require deep, interconnected knowledge remains largely uncharacterized. Metabolomics presents unique challenges with its complex biochemical pathways, heterogeneous identifier systems, and fragmented databases. To systematically evaluate LLM capabilities in this domain, we introduce MetaBench, the first benchmark for metabolomics assessment. Curated from authoritative public resources, MetaBench evaluates five capabilities essential for metabolomics research: knowledge, understanding, grounding, reasoning, and research. Our evaluation of 25 open- and closed-source LLMs reveals distinct performance patterns across metabolomics tasks: while models perform well on text generation tasks, cross-database identifier grounding remains challenging even with retrieval augmentation. Model performance also decreases on long-tail metabolites with sparse annotations. With MetaBench, we provide essential infrastructure for developing and evaluating metabolomics AI systems, enabling systematic progress toward reliable computational tools for metabolomics research.