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Paper Citation Record · LEDGER

Investigating Continual Pretraining in Large Language Models: Insights and Implications

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2402.17400.

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

pith.paper-citation-record.v1
2402.17400 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:31:26.018390Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T08:05:31.119035Z

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 01d858e5-6640-499b-a206-a0d3c17dce88 · inbound

Forward-Only Continual Learning cites this paper.

Forward-Only Continual Learning Investigating Continual Pretraining in Large Language Models: Insights and Implications

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-05T12:31:26.018390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:31:26.018390Z digest=sha256:30c343fea7f920a14f858508eb50dad62a4434a3f7a1e49d8aabb8a62cb468a8

Observation d33b2518-fa94-4d63-aaae-76b19973227b · inbound

Capacity-Aware Mixture Law Enables Efficient LLM Data Optimization cites this paper.

Capacity-Aware Mixture Law Enables Efficient LLM Data Optimization Investigating Continual Pretraining in Large Language Models: Insights and Implications

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-15T14:25:55.372535Z

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.

source=pdf_text observed=2026-05-15T14:23:30.849350Z digest=sha256:4185c89984f8eebb1844f6d98843310c8ca283f09680cfa5358d9ae558fbef0e

Observation e3077c5a-03e6-4806-9be3-5d9c28d9638f · inbound

Cortex-Inspired Continual Learning: Unsupervised Instantiation and Recovery of Functional Task Networks cites this paper.

Cortex-Inspired Continual Learning: Unsupervised Instantiation and Recovery of Functional Task Networks Investigating Continual Pretraining in Large Language Models: Insights and Implications

Reference 43

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T21:51:29.820067Z

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.

source=pdf_text observed=2026-05-08T04:03:46.242015Z digest=sha256:16c1f6b9b981cb9905e4c43354a8551db0abeb9793d6c85ed4980472d50fe21d

Observation 1796f0f4-2d41-40d4-b634-9a303d3e2c54 · inbound

Shortcut Solutions Learned by Transformers Impair Continual Compositional Reasoning cites this paper.

Shortcut Solutions Learned by Transformers Impair Continual Compositional Reasoning Investigating Continual Pretraining in Large Language Models: Insights and Implications

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T18:06:05.545554Z

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.

source=pdf_text observed=2026-05-08T16:42:08.322419Z digest=sha256:fd014bde614d39bf6594f49e7c3d2d9d3f7d30588a1668a62c910ce7e6437dec

Observation 0495b1ab-28d3-4b12-9a6c-f0032dde9d01 · inbound

Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm cites this paper.

Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm Investigating Continual Pretraining in Large Language Models: Insights and Implications

Reference 59

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T05:56:25.576711Z

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.

source=arxiv_source observed=2026-05-12T04:47:54.466097Z digest=sha256:bacb9693980f76984bd14faefb44779bec0f1b2c12e56fb8cec1e37fb284a314

Observation 5d1ecebc-f578-4012-81b4-be5a6bbc4e69 · inbound

Threat Modelling using Domain-Adapted Language Models: Empirical Evaluation and Insights cites this paper.

Threat Modelling using Domain-Adapted Language Models: Empirical Evaluation and Insights Investigating Continual Pretraining in Large Language Models: Insights and Implications

Reference 17

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T06:56:25.848549Z

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.

source=pdf_text observed=2026-05-12T03:50:46.260450Z digest=sha256:ca5ab8288d60bd8f8deed0bb24d377f2b0e5df20ca5f38af954bfbdeee5a059e

Observation 3ddcb007-9c69-4cee-93a4-3c19ac89bd04 · inbound

Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare cites this paper.

Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare Investigating Continual Pretraining in Large Language Models: Insights and Implications

Reference 10

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T08:05:31.121275Z

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.

source=arxiv_source observed=2026-07-01T08:00:40.906200Z digest=sha256:1be464de385d9217343f5bd1312f0daaaa1baa51c93cb321f6e3456e6f4602f9

Observation fc374ee4-8db5-4941-94ef-28f47914c581 · inbound

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD cites this paper.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Investigating Continual Pretraining in Large Language Models: Insights and Implications

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-01T10:42:42.659428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T10:42:42.659428Z digest=sha256:e22f50c4a7aa021ca33d4f904559a423b4aaf3247a07c5e77f7c9ac812be46e5

Observation 1f2d48b4-08a9-476a-88d7-11bdaff698cb · inbound

The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability cites this paper.

The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability Investigating Continual Pretraining in Large Language Models: Insights and Implications

Reference 2000

Resolution
malformed identifier
no resolver link, observed 2026-08-01T10:18:19.834210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T10:18:19.834210Z digest=sha256:3f47297e985b6331c0112e6582eed5a06189921fe64b78b725ceec7ddb80c3f8