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ARXIV:2603.24422 · SEARCH_RETRIEVAL · SUBMITTED 26 MAR · 20:30 UTC · FRESHNESS STALE
ARXIV:2603.24422SEARCH_RETRIEVALSUBMITTED 26 MAR · 20:30 UTCFRESHNESS STALEBen Chen · Siyuan Wang · Yufei Ma · Zihan Liang · Xuxin Zhang · Yue Lv · +17 at arXiv
OneSearch-V2 enhances e-commerce search with reasoning and self-distillation, boosting conversion rates and reducing search biases.
Opportunity summary
Pain OneSearch-V2 enhances e-commerce search with reasoning and self-distillation, boosting conversion rates and reducing search biases.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence unverified
OneSearch-V2 enhances e-commerce search with reasoning and self-distillation, boosting conversion rates and reducing search biases. Compared to multi-stage cascaded architecture, it offers advantages such as end-to-end joint optimization and high computational efficiency.
Generative Retrieval (GR) has emerged as a promising paradigm for modern search systems. Compared to multi-stage cascaded architecture, it offers advantages such as end-to-end joint optimization and high computational efficiency.
ScienceToStartup currently rates this 9.0/10 on the public viability pass. It contains three key innovations: (1) a thought-augmented complex query understanding module, which enables deep query understanding and overcomes the shallow semantic matching limitations…
search_retrieval moved forward this cycle; last verified April 2026. Public score 9.0/10. Implementation evidence is present through a linked repository.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score9.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
OneSearch-V2 enhances e-commerce search with reasoning and self-distillation, boosting conversion rates and reducing search biases.
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10.48550/arXiv.2603.24422OneSearch-V2 enhances e-commerce search with reasoning and self-distillation, boosting conversion rates and reducing search biases.
Abstract
Generative Retrieval (GR) has emerged as a promising paradigm for modern search systems. Compared to multi-stage cascaded architecture, it offers advantages such as end-to-end joint optimization and high computational efficiency. OneSearch, as a representative industrial-scale deployed generative search framework, has brought significant commercial and operational benefits. However, its inadequate understanding of complex queries, inefficient exploitation of latent user intents, and overfitting to narrow historical preferences have limited its further performance improvement. To address these challenges, we propose \textbf{OneSearch-V2}, a latent reasoning enhanced self-distillation generative search framework. It contains three key innovations: (1) a thought-augmented complex query understanding module, which enables deep query understanding and overcomes the shallow semantic matching limitations of direct inference; (2) a reasoning-internalized self-distillation training pipeline, which uncovers users' potential yet precise e-commerce intentions beyond log-fitting through implicit in-context learning; (3) a behavior preference alignment optimization system, which mitigates reward hacking arising from the single conversion metric, and addresses personal preference via direct user feedback. Extensive offline evaluations demonstrate OneSearch-V2's strong query recognition and user profiling capabilities. Online A/B tests further validate its business effectiveness, yielding +3.98\% item CTR, +3.05\% buyer conversion rate, and +2.11\% order volume. Manual evaluation further confirms gains in search experience quality, with +1.65\% in page good rate and +1.37\% in query-item relevance. More importantly, OneSearch-V2 effectively mitigates common search system issues such as information bubbles and long-tail sparsity, without incurring additional inference costs or serving latency.
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What was readable
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Viability
Time to MVP
Commercial
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Preparing verified analysis
Dimensions overall score 9.0
PROBLEM
OneSearch-V2 enhances e-commerce search with reasoning and self-distillation, boosting conversion rates and reducing search biases. Compared to multi-stage cascaded architecture, it offers advantages such as end-to-end joint optimization and high computational efficiency.
METHOD
Generative Retrieval (GR) has emerged as a promising paradigm for modern search systems. Compared to multi-stage cascaded architecture, it offers advantages such as end-to-end joint optimization and high computational efficiency.
RESULT
ScienceToStartup currently rates this 9.0/10 on the public viability pass. It contains three key innovations: (1) a thought-augmented complex query understanding module, which enables deep query understanding and overcomes the shallow semantic matching limitations of direct infe...
WHY NOW
search_retrieval moved forward this cycle; last verified April 2026. Public score 9.0/10. Implementation evidence is present through a linked repository.
Online A/B tests further validate its business effectiveness, yielding +3.98% item CTR
Explicitly stated in abstract with specific numeric result from online A/B tests
partial
+3.05% buyer conversion rate
Directly stated in abstract with specific numeric evidence from A/B tests
partial
+1.37% in query-item relevance
Explicitly stated in abstract with specific numeric result from manual evaluation
partial
a thought-augmented complex query understanding module, which enables deep query understanding and overcomes the shallow semantic matching limitations of direct inference
Directly stated in abstract as a key innovation with specific functionality described
partial
a reasoning-internalized self-distillation training pipeline, which uncovers users' potential yet precise e-commerce intentions beyond log-fitting through implicit in-context learning
Directly stated in abstract as a key innovation with specific mechanism described
partial
OneSearch-V2 effectively mitigates common search system issues such as information bubbles and long-tail sparsity, without incurring additional inference costs or serving latency
Explicitly stated in abstract with specific benefits mentioned, though no direct evidence of 'effectively' is provided
partial
a behavior preference alignment optimization system, which mitigates reward hacking arising from the single conversion metric
Directly stated in abstract as a key innovation with specific problem addressed
partial
The approach might not scale well for non-e-commerce contexts without significant adaptation
Explicitly stated in analysis section as a caveat, though presented as possibility rather than certainty
partial
Online A/B tests further validate its business effectiveness, yielding +3.98% item CTR
Explicitly stated in abstract with specific numeric result from online A/B tests
partial
+3.05% buyer conversion rate
Direct numeric result reported in abstract from A/B testing
partial
+1.37% in query-item relevance
Specific numeric improvement reported in abstract from manual evaluation
partial
a thought-augmented complex query understanding module, which enables deep query understanding and overcomes the shallow semantic matching limitations of direct inference
Directly stated in abstract as a key innovation with specific functionality described
partial
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OneSearch-V2 enhances e-commerce search with reasoning and self-distillation, boosting conversion rates and reducing search biases.
Segment
search_retrieval
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Public code linked for build inspection
Commercial read
9.0/10 public viability
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proof status
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Buyer urgency is not verified from source.
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0 references, 0 sources, 50% evidence coverage.
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
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Map target operator, economic buyer, and procurement trigger.
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Refresh defensibility bars with source receipts.
Integration burden
missing
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No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
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Operator workflow not sourced.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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