Evidence Selection

Evidence selection asks which retrieved documents, passages, claims, or multimodal observations are sufficient to support a decision or answer. It matters in retrieval-augmented generation, long-context and video reasoning, clinical decision support, and systematic-review screening, where missing or irrelevant evidence can distort results. Methods include learned retrievers and rerankers, utility and uncertainty scoring, graph or discrete optimization, and agents that acquire, verify, or compress evidence under a cost or token budget. A central challenge is that topical relevance and answer accuracy do not guarantee complete, traceable support: evidence quality, model variability, and downstream effects need separate evaluation, and more or better-filtered evidence does not always improve outcomes.