Long-form video question answering hinges on locating brief, sparse evidence buried in minutes of dense action. Flattening the entire video into a single caption stream forces the LLM to sift through thousands of tokens, where decisive cues are easily drowned out by surrounding context. We present MSTree, a training-free framework that organizes captions into a Multi-Scale Evidence Tree. The tree is built by a coordinated dual-modal segmentation method that combines visual scene transitions and textual coherence under temporal contiguity, producing segments that are both visually and semantically consistent. Inference follows a mine-then-reason workflow: the LLM first scans coarse segment summaries to locate the most relevant event, then reasons over its fine-grained captions. When the first-round prediction falls below a confidence threshold, Contrastive Targeted Reflection (CTR) activates. The model articulates a discriminative gap that specifies what evidence is still missing to separate the top candidate answers, and an answer-anchoring constraint requires any newly retrieved evidence to both support the revised prediction and contradict the original. On EgoSchema, NExT-QA, and Video-MME, MSTree reaches 68.4%, 77.5%, and 55.7% accuracy, outperforming all training-free baselines.

Clinical notes often contain useful information not documented in structured data, but their unstructured nature can lead to critical patient-related information being missed. To increase the likelihood that this valuable information is utilized for patient care, algorithms that summarize notes into a problem list have been proposed. Focused on identifying medically-relevant entities in the free-form text, these solutions are often detached from a canonical ontology and do not allow downstream use of the detected text-spans. Mitigating these issues, we present here a system for generating a canonical problem list from medical notes, consisting of two major stages. At the first stage, annotation, we use a transformer model to detect all clinical conditions which are mentioned in a single note. These clinical conditions are then grounded to a predefined ontology, and are linked to spans in the text. At the second stage, summarization, we develop a novel algorithm that aggregates over the set of clinical conditions detected on all of the patient’s notes, and produce a concise patient summary that organizes their most important conditions.