Paper Citation Record · LEDGER

LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2505.04421.

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

pith.paper-citation-record.v1
2505.04421 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:35:48.326331Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f5272bea-8391-4ff8-919e-f5b9f0f2ef93 · inbound

RankMixer: Scaling Up Ranking Models in Industrial Recommenders cites this paper.

RankMixer: Scaling Up Ranking Models in Industrial Recommenders LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T15:35:48.326331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b05ebb0d-420c-4b98-ad19-8d3b83b1cbda · inbound

Towards Comprehensible Recommendation with Large Language Model Fine-tuning cites this paper.

Towards Comprehensible Recommendation with Large Language Model Fine-tuning LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T22:06:54.127007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb520842-0242-4c2f-8189-83f9edab332c · inbound

Large Foundation Model for Ads Recommendation cites this paper.

Large Foundation Model for Ads Recommendation LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T18:30:08.126493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 028018db-293e-448d-a0d2-a1d3f189f51b · inbound

Beyond Quality: Unlocking Diversity in Ad Headline Generation with Large Language Models cites this paper.

Beyond Quality: Unlocking Diversity in Ad Headline Generation with Large Language Models LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T16:19:06.669408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 861e8642-17d3-4e98-b50b-9ef5d43e0f64 · inbound

Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation cites this paper.

Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:03:00.477024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d843de02-f8d2-4229-9b1c-a92c4140b812 · inbound

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

TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:50:25.113206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a2efa215-8f3d-40c4-a8bf-bdd0c2947e90 · inbound

UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction cites this paper.

UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T11:07:54.465904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.