Paper Citation Record · LEDGER

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models

As of 9 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2505.17470.

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

pith.paper-citation-record.v1
2505.17470 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:50:06.224196Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

21 of 21 outbound references displayed

verified exact0
verified fuzzy1
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f82d69a4-2c43-4f10-8c7f-d4e2936b74d5 · outbound

This paper cites GPT-4 Technical Report.

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:03.732566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1711320-ff47-4e38-bd31-d2ace71fc2e2 · outbound

This paper cites PaLM 2 Technical Report.

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models PaLM 2 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:03.807371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd97f6c3-2d3d-47ea-aa5d-0744499b1dbb · outbound

This paper cites Qwen Technical Report.

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models Qwen Technical Report

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:03.912934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8640d88-aedb-494b-803b-98e7ec99171c · outbound

This paper cites ACM Transactions on Intelligent Systems and Technology15(3), 1–45 (2024).

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models ACM Transactions on Intelligent Systems and Technology15(3), 1–45 (2024)

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:04.032447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29faa032-a98d-472f-be82-824bfe28374f · outbound

This paper cites In: 2016 IEEE Conference on Computational In- telligence and Games (CIG).

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models In: 2016 IEEE Conference on Computational In- telligence and Games (CIG)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:07.109671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9632d22d-a9e5-4f83-8276-1c4e076efade · outbound

This paper cites Into the Unknown: Self-Learning Large Language Models.

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models Into the Unknown: Self-Learning Large Language Models

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T14:50:06.797955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 413b14db-df5e-45d4-a4ae-da33be469d06 · outbound

This paper cites Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?.

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:04.399257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c61ad8c-6919-4a20-bfb0-677b6301e2dc · outbound

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

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:04.534004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74951cd0-0c03-4330-b65d-6306f08dc623 · outbound

This paper cites Large Language Models Can Self-Improve.

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models Large Language Models Can Self-Improve

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:04.681143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98a0050b-3d4d-44c3-9581-3581bbe062ea · outbound

This paper cites Advances in neural information processing systems35, 22199–22213 (2022).

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models Advances in neural information processing systems35, 22199–22213 (2022)

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:04.814698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bbb2b3d-8171-4f17-b07b-18a11fac857f · outbound

This paper cites SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models.

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:04.938283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e00e71a1-3cc2-48f2-a605-d90a225bfe62 · outbound

This paper cites Advances in neural information processing sys- tems 35, 27730–27744 (2022).

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models Advances in neural information processing sys- tems 35, 27730–27744 (2022)

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:05.068197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1f3863e-7606-483e-a643-b3fc8dfbc7fe · outbound

This paper cites Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:05.227428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 967a256c-50f7-4a44-81ae-988be3bb945a · outbound

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

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models Gemini: A Family of Highly Capable Multimodal Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:05.375729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d7e7a39-7a9c-44dc-bc1f-c26cab310eaf · outbound

This paper cites Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing.

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:05.485099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d68c17e1-0187-48cd-99ae-1bfdf2d4185b · outbound

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

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:05.634753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4745dec6-6cfa-47b5-bc22-4574d9fc8025 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:05.739233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d86d357-28ce-433d-96dc-3895499c6597 · outbound

This paper cites InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration.

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T14:50:06.481788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bfebc896-f2f0-4cae-b1d3-5ce5060d56ae · outbound

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

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:05.965432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 850f283d-bf75-4853-83b5-789ed865ee88 · outbound

This paper cites Advances in neural information processing systems35, 24824–24837 (2022).

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models Advances in neural information processing systems35, 24824–24837 (2022)

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:06.086224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4574cc95-ba7d-417b-931c-51af450fc7d5 · outbound

This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T14:50:06.224196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.