NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search
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.
682d30a37812103582f50de4
Sunhao Dai
admin_assigned
2025-05-21T10:19:10.865Z
64db88993725f8d9a908c077
https://cdn-avatars.huggingface.co/v1/production/uploads/64db88993725f8d9a908c077/JZEGk0kius6mrANlwOWw9.jpeg
Sunhao Dai
user
KID-22
682d30a37812103582f50de5
Wenjie Wang
682d30a37812103582f50de6
Liang Pang
admin_assigned
2025-05-21T10:19:41.530Z
5fc356c5ea82dd667bb0ffd1
/avatars/20519be09267bb429f3ca0f996f9fdc1.svg
Liang Pang
user
pl8787
682d30a37812103582f50de7
Jun Xu
682d30a37812103582f50de8
See-Kiong Ng
682d30a37812103582f50de9
Ji-Rong Wen
admin_assigned
2025-05-21T10:19:55.385Z
64b8c89052b7353d8c6a1013
/avatars/cd59fffe81f6b07b4519540b8ff3d95f.svg
Ji-Rong Wen
user
jrwen
682d30a37812103582f50dea
Tat-Seng Chua
admin_assigned
2025-05-21T10:19:49.244Z
6570ae84c4993b8fb96f41a8
/avatars/21f7d79d46ac4df0ecff8eca7678b33f.svg
Tat-Seng Chua
user
chuats
NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search
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.
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.
User Debug Mode Shadow User Mode online adaptation offline update generative AI search fine-grained feedback