NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search
Authors: name: Sunhao Dai; name: Wenjie Wang; name: Liang Pang; name: Jun Xu; name: See-Kiong Ng; name: Ji-Rong Wen; name: Tat-Seng Chua
Published: 2025-05-20 17:59:13

Abstract: Generative AI search is reshaping information retrieval by offering
end-to-end answers to complex queries, reducing users' reliance on manually
browsing and summarizing multiple web pages. However, while this paradigm
enhances convenience, it disrupts the feedback-driven improvement loop that has
historically powered the evolution of traditional Web search. Web search can
continuously improve their ranking models by collecting large-scale,
fine-grained user feedback (e.g., clicks, dwell time) at the document level. In
contrast, generative AI search operates through a much longer search pipeline,
spanning query decomposition, document retrieval, and answer generation, yet
typically receives only coarse-grained feedback on the final answer. This
introduces a feedback loop disconnect, where user feedback for the final output
cannot be effectively mapped back to specific system components, making it
difficult to improve each intermediate stage and sustain the feedback loop. In
this paper, we envision NExT-Search, a next-generation paradigm designed to
reintroduce fine-grained, process-level feedback into generative AI search.
NExT-Search integrates two complementary modes: User Debug Mode, which allows
engaged users to intervene at key stages; and Shadow User Mode, where a
personalized user agent simulates user preferences and provides AI-assisted
feedback for less interactive users. Furthermore, we envision how these
feedback signals can be leveraged through online adaptation, which refines
current search outputs in real-time, and offline update, which aggregates
interaction logs to periodically fine-tune query decomposition, retrieval, and
generation models. By restoring human control over key stages of the generative
AI search pipeline, we believe NExT-Search offers a promising direction for
building feedback-rich AI search systems that can evolve continuously alongside
human feedback.