Paper Citation Record · LEDGER

Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2503.02453.

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

pith.paper-citation-record.v1
2503.02453 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 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 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:09:47.578469Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T10:54:36.728921Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 e39847f2-6426-4b20-9021-6eefeabf5de5 · inbound

Large Language Model as Universal Retriever in Industrial-Scale Recommender System cites this paper.

Large Language Model as Universal Retriever in Industrial-Scale Recommender System Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-09T10:09:47.578469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60e25a43-2c3c-4071-ab85-3909c9292d1b · inbound

EGA-V2: An End-to-end Generative Framework for Industrial Advertising cites this paper.

EGA-V2: An End-to-end Generative Framework for Industrial Advertising Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:05.281203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d598b4f-e1ee-4855-9b67-854f18f355f1 · inbound

EGA-V1: Unifying Online Advertising with End-to-End Learning cites this paper.

EGA-V1: Unifying Online Advertising with End-to-End Learning Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T14:14:50.066469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9bd7818-3e0d-4e93-814f-27ad3303de41 · inbound

Act-With-Think: Chunk Auto-Regressive Modeling for Generative Recommendation cites this paper.

Act-With-Think: Chunk Auto-Regressive Modeling for Generative Recommendation Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T21:40:50.962308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8832d3dd-e7b2-408d-aae2-c959017948dc · inbound

GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models cites this paper.

GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T19:06:07.048637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa488020-fed9-44a0-a940-1733b8180114 · inbound

Generative Recommendation with Semantic IDs: A Practitioner's Handbook cites this paper.

Generative Recommendation with Semantic IDs: A Practitioner's Handbook Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T12:01:13.388893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93cc1e40-3a42-42b7-a4e7-3cd9d5b33690 · inbound

DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusion cites this paper.

DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusion Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-05T18:36:44.371474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83110449-ae33-4541-8e70-6583f154f5d1 · inbound

S$^2$GR: Stepwise Semantic-Guided Reasoning in Latent Space for Generative Recommendation cites this paper.

S$^2$GR: Stepwise Semantic-Guided Reasoning in Latent Space for Generative Recommendation Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:24:12.888711Z

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 7a49d6a4-1850-40a3-85e7-825a8eea1e69 · inbound

AgenticRS-Architecture: System Design for Agentic Recommender Systems cites this paper.

AgenticRS-Architecture: System Design for Agentic Recommender Systems Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-14T23:23:15.929246Z

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 22cd096b-6ed8-432d-9f41-69e2954c017f · inbound

MBGR: Multi-Business Prediction for Generative Recommendation at Meituan cites this paper.

MBGR: Multi-Business Prediction for Generative Recommendation at Meituan Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-13T19:18:09.334826Z

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 7135fbcf-5a8b-4cbf-88e9-0aeb089f741c · inbound

GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation cites this paper.

GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:59:02.933041Z

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 6d84f43f-80e1-4c86-8358-f3f7e4a94511 · inbound

Birds of a Feather Cluster Nearby: a Proximity-Aware Geo-Codebook for Local Service Recommendation cites this paper.

Birds of a Feather Cluster Nearby: a Proximity-Aware Geo-Codebook for Local Service Recommendation Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:01:10.810525Z

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 3fd0dfcd-a753-419b-8ca3-a0a5f1b2fe2c · inbound

CapsID: Soft-Routed Variable-Length Semantic IDs for Generative Recommendation cites this paper.

CapsID: Soft-Routed Variable-Length Semantic IDs for Generative Recommendation Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:26:12.795252Z

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 4f32c19c-e1c1-4ba7-987d-2e5e85e474d0 · inbound

Conditional Memory Enhanced Item Representation for Generative Recommendation cites this paper.

Conditional Memory Enhanced Item Representation for Generative Recommendation Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:32:06.314157Z

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 8f5d6118-6dff-4a34-a142-3011cb824289 · inbound

Learning Variable-Length Tokenization for Generative Recommendation cites this paper.

Learning Variable-Length Tokenization for Generative Recommendation Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:03:15.130559Z

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 37dac388-11a7-421b-9f8e-1ab3d7c3a730 · inbound

LWGR: Lagrangian-Constrained Personalized World Knowledge for Generative Recommendation cites this paper.

LWGR: Lagrangian-Constrained Personalized World Knowledge for Generative Recommendation Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-21T01:33:56.567610Z

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 eb87be32-6324-4a5c-a79b-c682b9a6dfbf · inbound

EvoRec: Self Evolving Agentic Recommender Systems cites this paper.

EvoRec: Self Evolving Agentic Recommender Systems Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-06-30T10:54:36.730430Z

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 b5233031-76a7-4694-a778-5417e4ed463b · inbound

Beyond Item Order: Temporal Gap Tokenization for Generative Recommendation with Semantic IDs cites this paper.

Beyond Item Order: Temporal Gap Tokenization for Generative Recommendation with Semantic IDs Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-11T23:02:18.007239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 000584a6-15d0-48a1-9393-c653f4de96a3 · inbound

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation cites this paper.

Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T08:44:48.801375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9da94bcf-622a-4c97-b612-ee65f4c0523a · inbound

Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation cites this paper.

Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Reference 38

Resolution: unresolved; no resolver link, observed 2026-07-31T22:39:48.692497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.