Paper Citation Record · LEDGER

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders

As of 6 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2604.07739.

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

pith.paper-citation-record.v1
2604.07739 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:13:46.622289Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact2
verified fuzzy3
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cb6f5027-0082-4cea-9065-c0ce51a87b39 · outbound

This paper cites PinRec: Unified Generative Retrieval for Pinterest Recommender Systems.

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders PinRec: Unified Generative Retrieval for Pinterest Recommender Systems

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-06-23T02:11:57.528699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b31617fe-bd3b-4f12-a690-4e82fb95f721 · outbound

This paper cites Tallrec: An effective and efficient tuning framework to align large language model with recommendation.

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders Tallrec: An effective and efficient tuning framework to align large language model with recommendation

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:15:41.743836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cf9a5c35-fc64-4fb1-bdea-a306934e3eed · outbound

This paper cites When to retrain a machine learning model.

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders When to retrain a machine learning model

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T05:16:03.810543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3052213a-1880-47de-892e-3c85089c84ee · outbound

This paper cites W., and Xiong, C.

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders W., and Xiong, C

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T05:16:03.782792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d791141b-c651-40b9-beec-f310b799b1d9 · outbound

This paper cites DV365: Extremely Long User History Modeling at Instagram.

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders DV365: Extremely Long User History Modeling at Instagram

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:16:03.755352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ca670c97-084a-4cb0-8685-6177ae7e0d06 · outbound

This paper cites Greats: Online selection of high-quality data for llm training in every iteration.Advances in Neural Information Processing Systems, 37:131197–131223.

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders Greats: Online selection of high-quality data for llm training in every iteration.Advances in Neural Information Processing Systems, 37:131197–131223

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:24:10.765584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 05bae7ed-8f67-43e1-87cf-22af4b88def0 · outbound

This paper cites MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models.

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:16:03.771646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e1db897a-072d-4a38-842a-1f7082da5882 · outbound

This paper cites an unresolved cited work.

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-05-17T06:24:10.762405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1e0e2e63-a376-4b85-8984-380b69f31c9b · outbound

This paper cites an unresolved cited work.

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-05-17T06:24:10.771264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation aac45ae2-6a2e-45b2-b040-a57ba82e2deb · outbound

This paper cites User history context and compression.Recent production systems emphasize efficient representa- tion of long and evolving user histories.

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders User history context and compression.Recent production systems emphasize efficient representa- tion of long and evolving user histories

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:24:10.774175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cd43b946-593f-4aba-999f-d6cf55eae6b1 · outbound

This paper cites Continuous learning over time.Continuous learning research addresses when and how models should be updated as data distributions shift.

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders Continuous learning over time.Continuous learning research addresses when and how models should be updated as data distributions shift

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:24:10.768592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0b841c4d-14a6-447d-9781-12842eb5433e · outbound

This paper cites an unresolved cited work.

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-05-17T06:24:10.776952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

No inbound Pith citation observations are available.