Paper Citation Record · LEDGER

Towards Effective and Efficient Continual Pre-training of Large Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2407.18743.

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

pith.paper-citation-record.v1
2407.18743 v1

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 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:33:12.929690Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T17:07:41.572149Z

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 ce8b472c-8fca-4410-8233-2259c968e0e9 · inbound

A Survey of LLM $\times$ DATA cites this paper.

A Survey of LLM $\times$ DATA Towards Effective and Efficient Continual Pre-training of Large Language Models

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:12.929690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9965d01f-0f52-44ea-9354-bed045e93516 · inbound

Improving Continual Pre-training Through Seamless Data Packing cites this paper.

Improving Continual Pre-training Through Seamless Data Packing Towards Effective and Efficient Continual Pre-training of Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:00.220733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cff34c7-dafc-49db-99ac-11a1256c228a · inbound

Continual Learning for Generative AI: From LLMs to MLLMs and Beyond cites this paper.

Continual Learning for Generative AI: From LLMs to MLLMs and Beyond Towards Effective and Efficient Continual Pre-training of Large Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T00:40:17.481210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33a57148-0852-4ef1-a90e-6730ef895cbf · inbound

From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan cites this paper.

From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan Towards Effective and Efficient Continual Pre-training of Large Language Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:57:04.433935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 826ac21c-433b-4d5a-8db6-66741d53ae38 · inbound

MUTE-DSS: A Digital-Twin-Based Decision Support System for Minimizing Underwater Radiated Noise in Ship Voyage Planning cites this paper.

MUTE-DSS: A Digital-Twin-Based Decision Support System for Minimizing Underwater Radiated Noise in Ship Voyage Planning Towards Effective and Efficient Continual Pre-training of Large Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T05:22:33.036501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b694d56-da4a-4776-b934-7f61cd225798 · inbound

TFGN: Task-Free, Replay-Free Continual Pre-Training Without Catastrophic Forgetting at LLM Scale cites this paper.

TFGN: Task-Free, Replay-Free Continual Pre-Training Without Catastrophic Forgetting at LLM Scale Towards Effective and Efficient Continual Pre-training of Large Language Models

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-19T17:07:41.573784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.