Paper Citation Record · LEDGER

Simple and Scalable Strategies to Continually Pre-train Large Language Models

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 36 inbound Pith citation observations for arXiv:2403.08763.

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

pith.paper-citation-record.v1
2403.08763 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 36 of 36 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:28:06.132947Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T18:40:03.364876Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
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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 7cefc8d9-d899-4498-999f-59cd614e20cb · inbound

DataComp-LM: In search of the next generation of training sets for language models cites this paper.

DataComp-LM: In search of the next generation of training sets for language models Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 87

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T22:58:17.011400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9d1a3c41-6262-40ba-8d3a-7e2dcbd5aa77 · inbound

Optimization Hyper-parameter Laws for Large Language Models cites this paper.

Optimization Hyper-parameter Laws for Large Language Models Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-23T20:45:48.824942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f013f16e-9803-4efe-9f5e-55e1b6ac7ed8 · inbound

The Zamba2 Suite: Technical Report cites this paper.

The Zamba2 Suite: Technical Report Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 26

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Source-reported events for the cited work

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Observation 88bf8412-69da-4847-9787-b368f85d46f5 · inbound

Yi-Lightning Technical Report cites this paper.

Yi-Lightning Technical Report Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 20

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Source-reported events for the cited work

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Observation 779549c5-4260-480a-b8f5-524388280719 · inbound

How to Merge Your Multimodal Models Over Time? cites this paper.

How to Merge Your Multimodal Models Over Time? Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 23

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Source-reported events for the cited work

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Observation 8fcd72c7-5c97-4060-ab9b-9ccbb8a3c978 · inbound

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs cites this paper.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.786182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a122b23e-ac11-46a5-a1ea-d759b41a5827 · inbound

ModelGrow: Continual Text-to-Video Pre-training with Model Expansion and Language Understanding Enhancement cites this paper.

ModelGrow: Continual Text-to-Video Pre-training with Model Expansion and Language Understanding Enhancement Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T01:04:35.150244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fea82ab-7e43-428e-8d50-3caf85992dce · inbound

Finite Horizon Optimization: Framework and Applications cites this paper.

Finite Horizon Optimization: Framework and Applications Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T23:12:21.442808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9393201d-8b51-4e5f-97ba-83bd51ba4db2 · inbound

TiEBe: Tracking Language Model Recall of Notable Worldwide Events Through Time cites this paper.

TiEBe: Tracking Language Model Recall of Notable Worldwide Events Through Time Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32c3eda3-14ab-4ce6-820c-2666f8d054b3 · inbound

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection cites this paper.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef218ce0-69f0-4f5e-8d2c-c21dbc223ad5 · inbound

WenyanGPT: A Large Language Model for Classical Chinese Tasks cites this paper.

WenyanGPT: A Large Language Model for Classical Chinese Tasks Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T05:28:06.132947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 971084e1-ad65-4afd-9984-61c550e5baeb · inbound

Bielik 11B v2 Technical Report cites this paper.

Bielik 11B v2 Technical Report Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T00:58:14.802643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b23974ec-96ff-4b76-93b3-f582f26594f0 · inbound

Bielik v3 Small: Technical Report cites this paper.

Bielik v3 Small: Technical Report Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:22.092014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c87e7829-d4d4-42bb-b8a4-3c06c09805b7 · inbound

Full-Parameter Continual Pretraining of Gemma2: Insights into Fluency and Domain Knowledge cites this paper.

Full-Parameter Continual Pretraining of Gemma2: Insights into Fluency and Domain Knowledge Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:45.874320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c718bbd-17db-4aa6-b20e-c781404f3d96 · inbound

Scalable Strategies for Continual Learning with Replay cites this paper.

Scalable Strategies for Continual Learning with Replay Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T20:36:39.784001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a349dfa-8ce7-4ec9-9dcc-80c8a642da10 · inbound

GEM: Empowering LLM for both Embedding Generation and Language Understanding cites this paper.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:51.041868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c26b75f9-a895-4929-9467-2237ca90adeb · inbound

The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text cites this paper.

The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:44.222974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3048fa1f-5a21-4bc3-a5d6-aa3a36ce037e · inbound

BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP cites this paper.

BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T04:21:41.944726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07dc000e-6f66-4c22-ae45-3bb023404480 · inbound

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence cites this paper.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T00:43:52.260130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 038d3a02-3d8b-4bf6-af0a-ff42eb81bfff · inbound

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 140

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:33.358434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0636fc25-2837-42bf-a493-1f01ffbe242e · inbound

Dynamic Chunking for End-to-End Hierarchical Sequence Modeling cites this paper.

Dynamic Chunking for End-to-End Hierarchical Sequence Modeling Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:59.479358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37f53f8d-f51c-490d-bc38-47243f20455b · inbound

ReaLM: Reflection-Enhanced Autonomous Reasoning with Small Language Models cites this paper.

ReaLM: Reflection-Enhanced Autonomous Reasoning with Small Language Models Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T17:30:10.099822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd450b25-3e02-456f-af4d-247139eba14d · inbound

Weight Decay Improves Language Model Plasticity cites this paper.

Weight Decay Improves Language Model Plasticity Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T00:15:37.298100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34d4f8d6-f342-4a48-813e-0626191389b9 · inbound

ZAYA1-8B Technical Report cites this paper.

ZAYA1-8B Technical Report Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 57

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T17:26:04.976053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8d2d12dd-2b34-40d7-84cc-588f2a95cd6a · inbound

HEBATRON: A Hebrew-Specialized Open-Weight Mixture-of-Experts Language Model cites this paper.

HEBATRON: A Hebrew-Specialized Open-Weight Mixture-of-Experts Language Model Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T01:52:05.147067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a205da5e-2bc2-4581-91a4-1cd302fa003d · inbound

A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM$\Delta$ Integration into Upcycled MoE cites this paper.

A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM$\Delta$ Integration into Upcycled MoE Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T11:13:13.593293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7e091b5d-6cf6-4b4f-b32b-53610f973f6f · inbound

STELLAR: Scaling 3D Perception Large Models for Autonomous Driving cites this paper.

STELLAR: Scaling 3D Perception Large Models for Autonomous Driving Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 62

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T07:14:02.656832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c341e0be-7e93-4fa5-ae6b-ab7e07f9e87c · inbound

RAGe: A Retrieval-Augmented Generation Evaluation Framework cites this paper.

RAGe: A Retrieval-Augmented Generation Evaluation Framework Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T12:24:39.979296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1ba6105b-ce2c-44ce-8b0c-473255862c57 · inbound

SupraBench: A Benchmark for Supramolecular Chemistry cites this paper.

SupraBench: A Benchmark for Supramolecular Chemistry Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 73

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T13:48:20.839464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0e975219-56d7-4054-bb91-6b67d87a5fb2 · inbound

How Post-Training Shapes Biological Reasoning Models cites this paper.

How Post-Training Shapes Biological Reasoning Models Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 65

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T07:55:31.044759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 23891e67-e3e1-4a76-8c8e-c339770ca2e6 · inbound

ZONOS2 Technical Report cites this paper.

ZONOS2 Technical Report Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 83

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T18:40:03.366519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7d2d0d6a-5fad-4d9c-88a2-1a6fc430256f · inbound

ZONOS2 Technical Report cites this paper.

ZONOS2 Technical Report Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 83

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T18:15:58.908150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 708e1389-ec1e-47d4-acc4-1a029c23e117 · inbound

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training cites this paper.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-14T08:04:06.432613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47902dca-dd64-4e65-88e4-40680c942b8f · inbound

ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution cites this paper.

ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-01T09:51:54.198135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15dc99fc-9770-442d-ba99-c3e8b7bf4550 · inbound

Continual Learning in Transition cites this paper.

Continual Learning in Transition Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T12:24:00.233555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6991873-2ff5-4e28-9cc5-bf0e384fdfc1 · inbound

Continual Learning in Transition cites this paper.

Continual Learning in Transition Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T14:38:35.380281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.