Paper Citation Record · LEDGER

Wukong: Towards a Scaling Law for Large-Scale Recommendation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 40 inbound Pith citation observations for arXiv:2403.02545.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2403.02545 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 40 of 40 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T20:15:14.134768Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T14:49:56.190893Z

Reference resolution

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verified exact0
verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f1521fca-0268-497f-9904-7eaa8225ee3d · inbound

Climber: Toward Efficient Scaling Laws for Large Recommendation Models cites this paper.

Climber: Toward Efficient Scaling Laws for Large Recommendation Models Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T20:15:14.134768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccab2f53-d515-41a3-bc84-b7e1a0574e05 · inbound

Privacy Preserving Conversion Modeling in Data Clean Room cites this paper.

Privacy Preserving Conversion Modeling in Data Clean Room Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T15:32:44.432163Z

Source-reported events for the cited work

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Observation 4aa05ce0-2fd7-4a8c-986a-a4f57ebb8e67 · inbound

MTGR: Industrial-Scale Generative Recommendation Framework in Meituan cites this paper.

MTGR: Industrial-Scale Generative Recommendation Framework in Meituan Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:05.097537Z

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Observation d33d534a-076f-4131-b88e-4ca2ae332e40 · inbound

DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR Prediction cites this paper.

DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR Prediction Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T14:22:59.304701Z

Source-reported events for the cited work

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Observation e0879cae-678c-462f-9b60-905a9d434649 · inbound

Yambda-5B -- A Large-Scale Multi-modal Dataset for Ranking And Retrieval cites this paper.

Yambda-5B -- A Large-Scale Multi-modal Dataset for Ranking And Retrieval Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T13:16:36.031738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44e66754-f055-4bdf-80c7-bc0c07f45495 · inbound

Scaling Transformers for Discriminative Recommendation via Generative Pretraining cites this paper.

Scaling Transformers for Discriminative Recommendation via Generative Pretraining Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T11:02:30.035644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a32631d-263b-4f42-856d-1795b913766e · inbound

From Scaling to Structured Expressivity: Rethinking Transformers for CTR Prediction cites this paper.

From Scaling to Structured Expressivity: Rethinking Transformers for CTR Prediction Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T22:08:57.735718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53021be9-13a5-4969-a22d-9266da003530 · inbound

SilverTorch: A Unified Model-based System to Democratize Large-Scale Recommendation on GPUs cites this paper.

SilverTorch: A Unified Model-based System to Democratize Large-Scale Recommendation on GPUs Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:22:04.531843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 12bec119-2815-42f1-8a77-e766cf650bb1 · inbound

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta cites this paper.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T13:45:25.951325Z

Source-reported events for the cited work

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Observation da2068eb-3f62-4f41-a024-26deb623c247 · inbound

When Less is More: The LLM Scaling Paradox in Context Compression cites this paper.

When Less is More: The LLM Scaling Paradox in Context Compression Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-16T02:30:32.257297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 617fce3a-384a-4adb-8c59-a4f044f1e296 · inbound

Compute Only Once: UG-Separation for Efficient Large Recommendation Models cites this paper.

Compute Only Once: UG-Separation for Efficient Large Recommendation Models Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:14:12.309739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c39f04fe-842b-4b85-aceb-3c3ab857db40 · inbound

Hi-SAM: A Hierarchical Structure-Aware Multi-modal Framework for Large-Scale Recommendation cites this paper.

Hi-SAM: A Hierarchical Structure-Aware Multi-modal Framework for Large-Scale Recommendation Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T00:06:27.986078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42ec0998-64a5-4f04-899e-8509feb40881 · inbound

Joint Model Parameter Scaling and Universal-Domain Data Integration for E-commerce Search Ranking cites this paper.

Joint Model Parameter Scaling and Universal-Domain Data Integration for E-commerce Search Ranking Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:55:26.174425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6eb5d488-2276-4db2-8bd2-607d1ca4724d · inbound

Beyond Dense Connectivity: Explicit Sparsity for Scalable Recommendation cites this paper.

Beyond Dense Connectivity: Explicit Sparsity for Scalable Recommendation Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:41:04.321836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ae36351e-65f7-4037-bc9f-16bab061b6b7 · inbound

IAT: Instance-As-Token Compression for Historical User Sequence Modeling in Industrial Recommender Systems cites this paper.

IAT: Instance-As-Token Compression for Historical User Sequence Modeling in Industrial Recommender Systems Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:01:01.341983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation be9d94f8-e9bb-4290-9df0-a517811bd22c · inbound

SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling cites this paper.

SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:11:03.220068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 45c57395-6aa3-48e0-8666-b4a676b7fd40 · inbound

TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds cites this paper.

TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:50:25.143880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e9b22ca1-4139-4d58-88d3-da9f3037e902 · inbound

Sample Is Feature: Beyond Item-Level, Toward Sample-Level Tokens for Unified Large Recommender Models cites this paper.

Sample Is Feature: Beyond Item-Level, Toward Sample-Level Tokens for Unified Large Recommender Models Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:17:37.079460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e6287a64-c9fa-4619-a7ea-e8e60d0002b0 · inbound

Sample Is Feature: Beyond Item-Level, Toward Sample-Level Tokens for Unified Large Recommender Models cites this paper.

Sample Is Feature: Beyond Item-Level, Toward Sample-Level Tokens for Unified Large Recommender Models Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:45:25.742511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 70d6b00b-fc75-4773-96a2-389c4e20eb7b · inbound

Sample Is Feature: Beyond Item-Level, Toward Sample-Level Tokens for Unified Large Recommender Models cites this paper.

Sample Is Feature: Beyond Item-Level, Toward Sample-Level Tokens for Unified Large Recommender Models Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T16:11:31.107903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58b15122-9cb4-4fab-85c9-73622caba7dc · inbound

LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction cites this paper.

LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:26:04.241119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b11b7e39-22b8-415e-94ab-6eb5c30f5b0b · inbound

Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models cites this paper.

Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-16T02:40:30.816740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 08248dbb-bd52-4656-bc27-e5750f5f5707 · inbound

FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost cites this paper.

FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:46:37.707866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ee59664-7f09-449e-894e-a7b54cba8693 · inbound

Harmonizing Generative Retrieval and Ranking in Chain-of-Recommendation cites this paper.

Harmonizing Generative Retrieval and Ranking in Chain-of-Recommendation Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-12T00:36:14.972519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 64cfb262-fabc-4a7a-8c46-da2508b6fcc8 · inbound

Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective cites this paper.

Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:51:25.449406Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 99f88517-cc28-45e4-92b6-ddfee3cd174e · inbound

PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents cites this paper.

PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:00:56.429073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3023e0a1-9bb3-4b75-bf5b-62e6641b547e · inbound

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:06:28.087364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 33216e99-ccd1-4490-8f9c-01d1e0acd3b7 · inbound

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-15T04:59:46.160957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dcab4dd8-7dd3-49ac-b350-7b092c624133 · inbound

FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation cites this paper.

FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:54:59.111983Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a042dbd4-0c9b-40db-aaa4-f64ccb8fa113 · inbound

Dual-Stream MLP is All You Need for CTR Prediction cites this paper.

Dual-Stream MLP is All You Need for CTR Prediction Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:26:54.273403Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8be474f2-9b3b-4445-aa5d-de162dfadd7c · inbound

Scaling Laws for Behavioral Foundation Models over User Event Sequences cites this paper.

Scaling Laws for Behavioral Foundation Models over User Event Sequences Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:36:45.308953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 071110ee-6878-409f-ba8d-ec22071d22f0 · inbound

DeRes: Decoupling Residual Stability and Adaptivity for Scalable CTR Prediction cites this paper.

DeRes: Decoupling Residual Stability and Adaptivity for Scalable CTR Prediction Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:57:25.795945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3cea4162-9c89-40cd-ae3c-7aea7f39def7 · inbound

The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenders cites this paper.

The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenders Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 91

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:29:42.031455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9fe6962d-4d76-4f04-a2c9-3b9848afe001 · inbound

UniFormer: Efficient and Unified Model-Centric Scaling for Industrial Recommendation cites this paper.

UniFormer: Efficient and Unified Model-Centric Scaling for Industrial Recommendation Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-04T14:49:56.192790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a3b8b0b2-36b9-4b9a-8415-8da9ce48d7c4 · inbound

CMSL: Constructive Multi-Sequence Learning for Recommendation Systems cites this paper.

CMSL: Constructive Multi-Sequence Learning for Recommendation Systems Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 107

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T16:05:49.808444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d717436-a277-4e7c-a253-711f8363a27f · inbound

CMSL: Constructive Multi-Sequence Learning for Recommendation Systems cites this paper.

CMSL: Constructive Multi-Sequence Learning for Recommendation Systems Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27ba684f-e28e-4efe-ba07-3e81b91bf4ac · inbound

TMallGS: Scaling Unified Feature and Sequence Modeling for Generative E-commerce Search cites this paper.

TMallGS: Scaling Unified Feature and Sequence Modeling for Generative E-commerce Search Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97739465-9ebf-4e39-9a4f-95fdff6d24d5 · inbound

Mosaic: A Fleet of User Embedding Specialists for Recommendation at Meta cites this paper.

Mosaic: A Fleet of User Embedding Specialists for Recommendation at Meta Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-31T23:18:22.049182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 851af5f9-57c3-40e6-a713-6a833212107b · inbound

Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence cites this paper.

Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-31T14:57:41.285910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6e5a83d-c169-4765-8686-35948aa8125b · inbound

WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models cites this paper.

WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T12:13:39.066464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.