Efficient Cross-Architecture Knowledge Transfer for Large-Scale Online User Response Prediction

Efficient Cross-Architecture Knowledge Transfer for Large-Scale Online User Response Prediction | Signal Canvas | ScienceToStartup

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Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-03-17
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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Canonical ID efficient-cross-architecture-knowledge-transfer-for-large-scale-online-user-response-prediction | Route /signal-canvas/efficient-cross-architecture-knowledge-transfer-for-large-scale-online-user-response-prediction

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Evidence Receipt

Route status: building

Claims: 7

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Efficient Cross-Architecture Knowledge Transfer for Large-Scale Online User Response Prediction

PDF: https://arxiv.org/pdf/2602.01775v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-03-17T21:43:58.792Z

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Watch and verify: Efficient Cross-Architecture Knowledge Transfer for Large-Scale Online User Response Prediction

/buildability/efficient-cross-architecture-knowledge-transfer-for-large-scale-online-user-response-prediction

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Subject: Efficient Cross-Architecture Knowledge Transfer for Large-Scale Online User Response Prediction

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Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
We propose CrossAdapt, a two-stage framework for efficient cross-architecture knowledge transfer.
Implicationpartial
The abstract explicitly states the proposal of 'CrossAdapt, a two-stage framework for efficient cross-architecture knowledge transfer.'
Verificationpartial
partial
Evidencepartial
The offline stage enables rapid embedding transfer via dimension-adaptive projections without iterative training, combined with progressive network distillation and strategic sampling to reduce computational cost.
Implicationpartial
The abstract clearly describes the function of the offline stage, mentioning 'rapid embedding transfer via dimension-adaptive projections without iterative training'.
Verificationpartial
partial
Evidencepartial
Experiments on three public datasets show that CrossAdapt achieves 0.27-0.43% AUC improvements while reducing training time by 43-71%.
Implicationpartial
The abstract provides specific quantitative results for AUC improvements on public datasets.
Verificationpartial
partial
Evidencepartial
Experiments on three public datasets show that CrossAdapt achieves 0.27-0.43% AUC improvements while reducing training time by 43-71%.
Implicationpartial
The abstract provides specific quantitative results for training time reduction on public datasets.
Verificationpartial
partial
Evidencepartial
The online stage introduces asymmetric co-distillation, where students update frequently while teachers update infrequently, together with a distribution-aware adaptation mechanism that dynamically balances historical knowledge preservation and fast adaptation to evolving data.
Implicationpartial
The abstract explicitly mentions the introduction of 'asymmetric co-distillation' in the online stage.
Verificationpartial
partial
Evidencepartial
Large-scale deployment on Tencent WeChat Channels (~10M daily samples) further demonstrates its effectiveness, significantly mitigating AUC degradation, LogLoss increase, and prediction bias compared to standard distillation baselines.
Implicationpartial
The abstract reports on large-scale deployment and its positive impact on AUC degradation.
Verificationpartial
partial
Evidencepartial
Existing knowledge distillation methods struggle with architectural heterogeneity and the prohibitive cost of transferring large embedding tables.
Implicationpartial
The abstract identifies a limitation of existing methods, which CrossAdapt aims to address.
Verificationpartial
partial

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Efficient Cross-Architecture Knowledge Transfer for Large-Scale Online User Response Prediction

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