EOR: Evidence-Obligation Reasoning for Verification-Driven Long-Video Question Answering

Long-video question answering requires reasoning over sparse and temporally dispersed evidence. A central challenge is that retrieving video segments relevant to a question does not necessarily provide sufficient evidence to justify an answer. Existing retrieval-based methods mainly determine what to inspect from question relevance or intermediate reasoning cues, while recent hypothesis-verification approaches make evidence acquisition more goal-directed but typically do not maintain explicit, persistent verification requirements that govern subsequent reasoning.We propose EOR, an Evidence-Obligation Reasoning framework that formulates long-video QA as iterative hypothesis verification. Each candidate hypothesis is associated with persistent evidence obligations, i.e., observable conditions that should be established before the hypothesis is sufficiently grounded. Their states are maintained across reasoning rounds, allowing unresolved, contradicted, and discriminative requirements to determine what evidence should be inspected next. EOR uses a lightweight temporal event graph to localize candidate regions, verifies the corresponding source-video evidence, and allocates verification effort across a compact set of complementary hypotheses rather than repeatedly inspecting redundant candidates. For open-ended QA, accumulated verified evidence can further revise an inadequate hypothesis space instead of merely re-ranking a fixed set of initial candidates. We evaluate EOR in both multiple-choice and open-ended long-video question answering, examining answer quality, evidence-seeking efficiency, and robustness to incomplete initial hypotheses.