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What Should We Ask Next? Retrieval-Aware Question Learning under Partial Evidence

Paper recorded by Signals 4 on 2026-09-18 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.AI · 人工智能 · first seen 2026-09-21

Abstract

Interactive retrieval under partial evidence is a sequential information-acquisition problem: an agent must decide which question will create the most useful evidence for the next retrieval update. Existing systems train this decision by imitating an offline ordering of candidate QA pairs, although question value is determined by the response it elicits and its downstream effect on retrieval. We e

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#12 most recent of 320 cs.AI papers we have recorded · ↑ newer: AutoViewMem: Self-Configuring Orthogonal Views for Conversational Long · ↓ older: Detecting Pretraining Data in Large Language Models from a Free-Energy
Cite this page: What Should We Ask Next? Retrieval-Aware Question Learning under Partial Evidence: the #12 most recent of 320 cs.AI papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/what-should-we-ask-next-retrieval-aware-question-learning-under-partial-evidence.html
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