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Paper Citation Record · LEDGER

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding

As of 20 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2505.04723.

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

pith.paper-citation-record.v1
2505.04723 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:27:14.768095Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

28 of 28 outbound references displayed

  • verified exact0
  • verified fuzzy11
  • unresolved17
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aef93fac-bedf-459d-b692-fa7a1b702d40 · outbound

This paper cites In: International Conference on Services Computing.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding In: International Conference on Services Computing

Reference 1

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 7360241d-fca9-49bb-b1f3-ed183fdb3a82 · outbound

This paper cites Computer Stan- dards & Interfaces p.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding Computer Stan- dards & Interfaces p

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 6ab72c5e-4ffc-4a3c-bdb2-e054b5b2f9dc · outbound

This paper cites Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey

Reference 3

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Observation 5e58696d-92a3-451b-8e97-1c7cde77feca · outbound

This paper cites In: 2024 IEEE 17th International Conference on Signal Processing (ICSP).

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding In: 2024 IEEE 17th International Conference on Signal Processing (ICSP)

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 95bab909-0348-489c-b491-fe8862fe4fde · outbound

This paper cites In: ICASSP 2025-2025 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP).

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding In: ICASSP 2025-2025 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP)

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 79b08b38-0ca8-4a9d-8b25-1707fdc2ee61 · outbound

This paper cites Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective

Reference 6

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Reference 7

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Observation 92b6d827-f4f0-4c32-bc53-0835d6e9b835 · outbound

This paper cites In: Proceedings of the fourth ACM international conference on AI in finance.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding In: Proceedings of the fourth ACM international conference on AI in finance

Reference 8

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Observation 10418670-ef03-40c9-a1fd-ca2bc142644e · outbound

This paper cites Health Care Science 2(4), 255–263 (2023).

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding Health Care Science 2(4), 255–263 (2023)

Reference 9

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation a018c488-42be-4998-9b2d-43ee1469ef42 · outbound

This paper cites In: Pro- ceedings of the 26th annual international conference on machine learning.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding In: Pro- ceedings of the 26th annual international conference on machine learning

Reference 10

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Observation 2336e83c-a4c3-4147-b812-cffd7660ba99 · outbound

This paper cites In: Fu, X., Fleisig, E.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding In: Fu, X., Fleisig, E

Reference 11

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation ddc6db4a-0d8b-4919-98a4-a43cdc8be440 · outbound

This paper cites In: Al-Onaizan, Y., Bansal, M., Chen, Y.N.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding In: Al-Onaizan, Y., Bansal, M., Chen, Y.N

Reference 12

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 4441d46e-1f1f-4163-b63b-aa37e4d151f7 · outbound

This paper cites Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning

Reference 13

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Observation 7acb583f-7e21-4b76-9fd9-0280383bfe19 · outbound

This paper cites https:// huggingface.co/datasets/BAAI/Infinity-Instruct (2024), accessed: 2024-04- 08.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding https:// huggingface.co/datasets/BAAI/Infinity-Instruct (2024), accessed: 2024-04- 08

Reference 14

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Reference 15

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Reference 16

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Observation e3587fb8-9d6b-497f-954d-fcebfa565d38 · outbound

This paper cites In: International Conference on Machine Learning.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding In: International Conference on Machine Learning

Reference 17

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Observation 228d35f9-bf2c-4f0d-9a66-d80c783710ad · outbound

This paper cites https://github.com/apoorvumang/ prompt-lookup-decoding/ (November 2023), accessed: 2024-04-08.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding https://github.com/apoorvumang/ prompt-lookup-decoding/ (November 2023), accessed: 2024-04-08

Reference 18

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Reference 19

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Observation 791c098e-c1e0-4205-a32a-970ac1e31e02 · outbound

This paper cites In: Proceed- ings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding In: Proceed- ings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining

Reference 20

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Reference 21

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Reference 22

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Observation 9687c014-cdd3-4e44-90ff-f2a0829f8ace · outbound

This paper cites Efficient Sequence Packing without Cross-contamination: Accelerating Large Language Models without Impacting Performance.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding Efficient Sequence Packing without Cross-contamination: Accelerating Large Language Models without Impacting Performance

Reference 23

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Reference 24

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Reference 25

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Observation 771168a9-1241-4484-ad8e-53f7c6ccc7a9 · outbound

This paper cites Advances in Neural Information Processing Systems 36, 62991–63010 (2023).

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding Advances in Neural Information Processing Systems 36, 62991–63010 (2023)

Reference 26

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 26398668-2718-4bc6-b1c7-9a423bfa923f · outbound

This paper cites In: Proceedings of the 29th Symposium on Operating Systems Principles.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding In: Proceedings of the 29th Symposium on Operating Systems Principles

Reference 27

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 5edbe3eb-39e2-47fb-82a6-2b77836439a5 · outbound

This paper cites an unresolved cited work.

SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding Unresolved cited work

Reference 4572

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Pith citing papers

No inbound Pith citation observations are available.