Paper Citation Record · LEDGER

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning?

As of 11 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 1 inbound Pith citation observation for arXiv:2501.17840.

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

pith.paper-citation-record.v1
2501.17840 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:35:23.344498Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T17:36:30.486056Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:49:30.541804Z

Reference resolution

21 of 21 outbound references displayed

verified exact3
verified fuzzy0
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 89f30d12-beb7-455d-9348-f695ce1b115c · outbound

This paper cites online" 'onlinestring :=.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? online" 'onlinestring :=

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.271454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9984128-b972-47a8-9c02-194b355a8489 · outbound

This paper cites write newline.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? write newline

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.275752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7642aee-0ada-4aee-ad60-388f15a54fed · outbound

This paper cites an unresolved cited work.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-10T04:35:23.764342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c4ee52c8-910f-49c7-a8fc-fd37b54d5e56 · outbound

This paper cites LoRA Learns Less and Forgets Less.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? LoRA Learns Less and Forgets Less

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.283469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d8fc01e-fe53-4e2e-bf0a-60ef817ee4a2 · outbound

This paper cites Universal Self-Consistency for Large Language Model Generation.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? Universal Self-Consistency for Large Language Model Generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.287388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8673560e-d542-439b-b62c-0e4aa81d8fcd · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.291249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de8025ea-bd95-4745-a6b7-64c8535c0c41 · outbound

This paper cites Don't Stop Pretraining: Adapt Language Models to Domains and Tasks.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.294972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dbdc057-fbbc-4b4d-b145-33b72fc11e18 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? LoRA: Low-Rank Adaptation of Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.298691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 471c1a28-3810-4a6c-898a-c0475dd19181 · outbound

This paper cites GPT-4o System Card.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? GPT-4o System Card

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.302549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b29562d2-2e6e-4988-8a86-cad75fc7d827 · outbound

This paper cites Continual Pre-training of Language Models.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? Continual Pre-training of Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.306078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e0f957a-9078-4696-92dd-76e4c6622fbd · outbound

This paper cites Adapting a Language Model While Preserving its General Knowledge.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? Adapting a Language Model While Preserving its General Knowledge

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-10T04:35:23.679609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 235dcafb-e87b-41bf-ad6a-bb46a99c6391 · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.313436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba4fd7df-6605-42f7-bdd6-a447a197984a · outbound

This paper cites an unresolved cited work.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.316539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e214610-ae28-4486-81b8-d29467bb94bf · outbound

This paper cites GPT-4 Technical Report.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? GPT-4 Technical Report

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.319785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75e84618-4469-4981-b8fa-1fc18f079785 · outbound

This paper cites Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-10T04:35:23.563892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 14440930-e88a-424c-a1b3-9d343000d9cd · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? Gemini: A Family of Highly Capable Multimodal Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.326427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc92f5fe-1b19-42ba-b2dc-e5399a4ea0ff · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? LLaMA: Open and Efficient Foundation Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.330036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86920c38-f15a-4394-a91b-95e90458a029 · outbound

This paper cites an unresolved cited work.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.333611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32c41bbf-2bff-4f2e-9106-6acfa5dcce7c · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.337214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68f74576-2220-4f0c-9b27-a8db91292573 · outbound

This paper cites LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:23.340789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1be8632-bd08-4e52-b967-399e961d420a · outbound

This paper cites BUSTER: a "BUSiness Transaction Entity Recognition" dataset.

Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning? BUSTER: a "BUSiness Transaction Entity Recognition" dataset

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-10T04:35:23.383372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation a20210af-e41d-46c2-9602-5c968234b4dd · inbound

Train, Retrieve, or Both? A Four-Arm Head-to-Head for Correct Statutory Citation on the Ontario Residential Tenancies Act cites this paper.

Train, Retrieve, or Both? A Four-Arm Head-to-Head for Correct Statutory Citation on the Ontario Residential Tenancies Act Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning?

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:49:30.545325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.