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ARXIV:2603.24226 · SEARCH RANKING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.24226SEARCH RANKINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALELiren Yu · Caiyuan Li · Feiyi Dong · Tao Zhang · Zhixuan Zhang · Dan Ou · +2 at arXiv
UniScale co-designs data and model architecture to unlock superior performance in search ranking by synergistically scaling both elements.
Opportunity summary
Pain UniScale co-designs data and model architecture to unlock superior performance in search ranking by synergistically scaling both elements.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
UniScale co-designs data and model architecture to unlock superior performance in search ranking by synergistically scaling both elements. However, existing approaches focus mainly on architectural improvements, overlooking the critical synergy between data and architecture…
Recent advances in Large Language Models (LLMs) have inspired a surge of scaling law research in industrial search, advertising, and recommendation systems. However, existing approaches focus mainly on architectural improvements, overlooking the critical synergy…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments on large-scale real world E-commerce search platform demonstrate that UniScale achieves significant improvements through the synergistic co-design of data and architecture and…
Search Ranking moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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UniScale co-designs data and model architecture to unlock superior performance in search ranking by synergistically scaling both elements.
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10.48550/arXiv.2603.24226UniScale co-designs data and model architecture to unlock superior performance in search ranking by synergistically scaling both elements.
Abstract
Recent advances in Large Language Models (LLMs) have inspired a surge of scaling law research in industrial search, advertising, and recommendation systems. However, existing approaches focus mainly on architectural improvements, overlooking the critical synergy between data and architecture design. We observe that scaling model parameters alone exhibits diminishing returns, i.e., the marginal gain in performance steadily declines as model size increases, and that the performance degradation caused by complex heterogeneous data distributions is often irrecoverable through model design alone. In this paper, we propose UniScale to address these limitation, a novel co-design framework that jointly optimizes data and architecture to unlock the full potential of model scaling, which includes two core parts: (1) ES$^3$ (Entire-Space Sample System), a high-quality data scaling system that expands the training signal beyond conventional sampling strategies from both intra-domain request contexts with global supervised signal constructed by hierarchical label attribution and cross-domain samples aligning with the essence of user decision under similar content exposure environment in search domain; and (2) HHSFT (Heterogeneous Hierarchical Sample Fusion Transformer), a novel architecture designed to effectively model the complex heterogeneous distribution of scaled data and to harness the entire space user behavior data with Heterogeneous Hierarchical Feature Interaction and Entire Space User Interest Fusion, thereby surpassing the performance ceiling of structure-only model tuning. Extensive experiments on large-scale real world E-commerce search platform demonstrate that UniScale achieves significant improvements through the synergistic co-design of data and architecture and exhibits clear scaling trends, delivering substantial gains in key business metrics.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
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Dimensions overall score 7.0
PROBLEM
UniScale co-designs data and model architecture to unlock superior performance in search ranking by synergistically scaling both elements. However, existing approaches focus mainly on architectural improvements, overlooking the critical synergy between data and architecture desi...
METHOD
Recent advances in Large Language Models (LLMs) have inspired a surge of scaling law research in industrial search, advertising, and recommendation systems. However, existing approaches focus mainly on architectural improvements, overlooking the critical synergy between data and...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments on large-scale real world E-commerce search platform demonstrate that UniScale achieves significant improvements through the synergistic co-design of data and architecture and exhibi...
WHY NOW
Search Ranking moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
UniScale co-designs data and model architecture to unlock superior performance in search ranking by synergistically scaling both elements. However, existing approaches focus mainly on architectural improvements, overlooking the critical synergy between data and architecture design.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Recent advances in Large Language Models (LLMs) have inspired a surge of scaling law research in industrial search, advertising, and recommendation systems. However, existing approaches focus mainly on architectural improvements, overlooking the critical synergy between data and architecture design.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments on large-scale real world E-commerce search platform demonstrate that UniScale achieves significant improvements through the synergistic co-design of data and architecture and exhibits clear scaling trends, delivering substantial gains in key business metrics. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Search Ranking moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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UniScale co-designs data and model architecture to unlock superior performance in search ranking by synergistically scaling both elements.
Segment
Search Ranking
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Commercial read
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