title: NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search
summary: 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.
paper_authors._id: 682d30a37812103582f50de4
paper_authors.name: Sunhao Dai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-21T10:19:10.865Z
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paper_authors.fullname: Sunhao Dai
paper_authors.type: user
paper_authors.user: KID-22
paper_authors._id: 682d30a37812103582f50de5
paper_authors.name: Wenjie Wang
paper_authors._id: 682d30a37812103582f50de6
paper_authors.name: Liang Pang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-21T10:19:41.530Z
paper_authors._id: 5fc356c5ea82dd667bb0ffd1
paper_authors.avatarUrl: /avatars/20519be09267bb429f3ca0f996f9fdc1.svg
paper_authors.fullname: Liang Pang
paper_authors.type: user
paper_authors.user: pl8787
paper_authors._id: 682d30a37812103582f50de7
paper_authors.name: Jun Xu
paper_authors._id: 682d30a37812103582f50de8
paper_authors.name: See-Kiong Ng
paper_authors._id: 682d30a37812103582f50de9
paper_authors.name: Ji-Rong Wen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-21T10:19:55.385Z
paper_authors._id: 64b8c89052b7353d8c6a1013
paper_authors.avatarUrl: /avatars/cd59fffe81f6b07b4519540b8ff3d95f.svg
paper_authors.fullname: Ji-Rong Wen
paper_authors.type: user
paper_authors.user: jrwen
paper_authors._id: 682d30a37812103582f50dea
paper_authors.name: Tat-Seng Chua
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-21T10:19:49.244Z
paper_authors._id: 6570ae84c4993b8fb96f41a8
paper_authors.avatarUrl: /avatars/21f7d79d46ac4df0ecff8eca7678b33f.svg
paper_authors.fullname: Tat-Seng Chua
paper_authors.type: user
paper_authors.user: chuats
paper_title: NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search
paper_summary: 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.
paper_ai_summary: NExT-Search seeks to enhance generative AI search by introducing fine-grained feedback mechanisms at each stage of the pipeline using User Debug Mode and Shadow User Mode, facilitating continuous improvement.
paper_ai_keywords: User Debug Mode Shadow User Mode online adaptation offline update generative AI search fine-grained feedback