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Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Grounded question answering systems should answer only when the supplied evidence supports the answer. In multi-hop QA, this requirement is difficult because partial evidence can make an unsupported answer appear plausible. We study selective answering through evidence sufficiency boundaries: for the same question, a model should abstain under unsupported or partially supported context, answer when the context first becomes sufficient, and keep the answer stable when redundant evidence is added. We introduce Evidence Sufficiency Boundary Training, a generation-native training framework that co

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