Paper Citation Record · LEDGER

External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2502.17494.

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

pith.paper-citation-record.v1
2502.17494 v7

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T02:11:16.855391Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T04:59:46.227841Z

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 aa447446-6102-4575-936f-0a578732b607 · 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 External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:01:01.322795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a55a9fd8-79b9-4933-848a-1ff0c57b351e · inbound

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

SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:11:03.211781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1fa9cc51-ebae-47be-ba8d-7654f5b0ccc8 · inbound

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

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:06:27.950267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation af03f25e-eb37-4d0b-b1d2-463a8ed3bb71 · inbound

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

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-15T04:59:46.230797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 006e0979-5da3-478b-8cb7-6a85cd70cfd1 · inbound

ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation cites this paper.

ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T02:11:16.855391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06d1e7d7-7d7e-4c4d-9d53-1a21fd1722fc · inbound

Building a User Foundation Model for the Open Web cites this paper.

Building a User Foundation Model for the Open Web External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-31T20:16:40.334625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.