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Paper Citation Record · LEDGER

Monolith: Real Time Recommendation System With Collisionless Embedding Table

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2209.07663.

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

pith.paper-citation-record.v1
2209.07663 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:11:32.456543Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T06:55:26.150196Z

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 03dc2364-6a75-4294-87ab-ea5cbd37d152 · inbound

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google cites this paper.

Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google Monolith: Real Time Recommendation System With Collisionless Embedding Table

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-10T19:11:32.456543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:11:32.456543Z digest=sha256:7523085cb39d832d9e0235f4ff43c695e91e79bbe76d482dc3f947949ef8dcd3

Observation 12575196-fd6d-46f4-a34b-dc1f0bcf13dd · inbound

Reinforcement Learning from User Feedback cites this paper.

Reinforcement Learning from User Feedback Monolith: Real Time Recommendation System With Collisionless Embedding Table

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T15:31:18.796406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:31:18.796406Z digest=sha256:fd5815eb07cd462f4b36de595fb3833b2e8138a9a8fe92e3f3b0e8469e2210bf

Observation e20fe1db-385e-44f1-b4a3-caa7bc29b650 · inbound

DCN^2: Interplay of Implicit Collision Weights and Explicit Cross Layers for Large-Scale Recommendation cites this paper.

DCN^2: Interplay of Implicit Collision Weights and Explicit Cross Layers for Large-Scale Recommendation Monolith: Real Time Recommendation System With Collisionless Embedding Table

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T23:10:38.672478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:10:38.672478Z digest=sha256:d659e8c415abc788e34e7dac933743db4d766e64118154adfae67b8593310382

Observation 3332c38f-c220-4816-8c1c-8da4400e2aa8 · inbound

Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training cites this paper.

Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training Monolith: Real Time Recommendation System With Collisionless Embedding Table

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-05T16:20:16.148976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:20:16.148976Z digest=sha256:c7919daed5f919494a5ee12a12bac08fdebdf5895132242f06618c554b49f472

Observation 3933ef2b-3d2e-43ff-9480-749cb2a613e9 · inbound

Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest cites this paper.

Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest Monolith: Real Time Recommendation System With Collisionless Embedding Table

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-05T05:30:31.320766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:30:31.320766Z digest=sha256:5485bd9037ad7a477df8e998a1f4b704381576f541a11f7ea6418432f608c399

Observation e3accc95-0d03-4016-b8a0-d9bc660d249d · inbound

xGR: Efficient Generative Recommendation Serving at Scale cites this paper.

xGR: Efficient Generative Recommendation Serving at Scale Monolith: Real Time Recommendation System With Collisionless Embedding Table

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-03T16:54:17.370060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T16:54:17.370060Z digest=sha256:f1d539ed4e0b9c5aed71165017e86835fb044cda331050354dbdfde5d8eccf2e

Observation 0a64a2e2-b0e7-40bc-afa1-21e6290912de · inbound

Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders cites this paper.

Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders Monolith: Real Time Recommendation System With Collisionless Embedding Table

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-21T12:04:09.519473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-21T12:00:32.942950Z digest=sha256:6bfef7a1df80f15641c50a9050876cfe0415bdbcb317e006e936a697db9605d5

Observation 825368de-396f-4371-8357-d7ff14cb3e40 · inbound

UniScale: Synergistic Entire Space Data and Model Scaling for Search Ranking cites this paper.

UniScale: Synergistic Entire Space Data and Model Scaling for Search Ranking Monolith: Real Time Recommendation System With Collisionless Embedding Table

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-25T06:55:26.152677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-25T06:52:50.606682Z digest=sha256:6d58e77a30384cd6418788713f576391f966e8435f97112d2e2f4f0d3f12f78b

Observation 0110eda8-b2ad-4832-ab88-5b7de012c54e · inbound

A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems cites this paper.

A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems Monolith: Real Time Recommendation System With Collisionless Embedding Table

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-20T23:49:15.098338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-20T23:47:32.775347Z digest=sha256:a87f9fe87f930eeadf7b4885de7e21acdb8d20706632f51c3f17e1c43d605065

Observation 68fbfc97-3dd7-491f-b40e-39fd4337142e · inbound

Mutable Low-Rank Sketches for Retrain-Free Recommendation cites this paper.

Mutable Low-Rank Sketches for Retrain-Free Recommendation Monolith: Real Time Recommendation System With Collisionless Embedding Table

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-01T23:51:43.081334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T23:51:43.081334Z digest=sha256:94c395a23ab6216e9a2198d5da3f98e836458690da1d66b90c9d40f6b10944fa

Observation 1293744e-10f1-45f3-b68f-7cef5744d5eb · inbound

LO-FAR: A Cost-Aware Local Filter for Sparse Feature Ranking in Industrial Ad Recommendation cites this paper.

LO-FAR: A Cost-Aware Local Filter for Sparse Feature Ranking in Industrial Ad Recommendation Monolith: Real Time Recommendation System With Collisionless Embedding Table

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-01T09:09:37.663287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T09:09:37.663287Z digest=sha256:f106eea13c5e20d3ec594ccbc31901f811c4ff9186e85451c9b59678682f3856

Observation 98d2fcdf-a28e-4384-b71e-657eb68f32c0 · 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 Monolith: Real Time Recommendation System With Collisionless Embedding Table

Reference 18

Resolution
unresolved
no resolver link, observed 2026-07-31T23:18:21.748526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T23:18:21.748526Z digest=sha256:e19eeaccf31fdad59d5141ba57d7fb2dde27b319aaa481252c5ef7b2178b41a4