Paper Citation Record · LEDGER

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning

As of 16 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 2 inbound Pith citation observations for arXiv:2506.17264.

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

pith.paper-citation-record.v1
2506.17264 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:16:55.294429Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T23:42:00.965554Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T23:45:08.008153Z

Reference resolution

24 of 24 outbound references displayed

verified exact0
verified fuzzy14
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 29082aa9-fec8-49a7-8b4d-32360ecd7a35 · outbound

This paper cites Languagemodels are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Languagemodels are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:16:55.217026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5f55efd-4a1c-45dc-90e4-869f19ac5ce4 · outbound

This paper cites Boolq: Exploring the surprising difficulty of natural yes/no questions.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.501530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 84413e3d-e249-441c-8327-1ab3f5c2b89e · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:16:55.224774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 950d3682-8089-4fa9-9dfd-320d7ee3072f · outbound

This paper cites The commitmentbank: Investigating projection in naturally occurring discourse.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning The commitmentbank: Investigating projection in naturally occurring discourse

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.491889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation aeee722d-0e85-4577-b483-048cb84c3b7e · outbound

This paper cites Data augmentation using llms: Data per- spectives, learning paradigms and challenges.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Data augmentation using llms: Data per- spectives, learning paradigms and challenges

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.483478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4e500435-d1df-4ee1-b6bf-04cf1e042e56 · outbound

This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models.Nature Machine Intelligence, 5(3):220–235, 2023.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Parameter-efficient fine-tuning of large-scale pre-trained language models.Nature Machine Intelligence, 5(3):220–235, 2023

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:16:55.237162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b9b3438-8f48-44aa-a3d0-d2b56ef6370c · outbound

This paper cites Variance-reduced zeroth-order methods for fine-tuning language models.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Variance-reduced zeroth-order methods for fine-tuning language models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.469222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6e51eb9e-c793-4809-96cc-0d8364cbbe59 · outbound

This paper cites The Llama 3 Herd of Models.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning The Llama 3 Herd of Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T05:16:55.244598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bab63bb1-0931-4fba-a2e6-ec71b2bed79d · outbound

This paper cites Gardner, Osbert Bastani, Christopher De Sa, Xiaodong Yu, Beidi Chen, and Zhaozhuo Xu.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Gardner, Osbert Bastani, Christopher De Sa, Xiaodong Yu, Beidi Chen, and Zhaozhuo Xu

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.460056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 88d0b947-a205-4153-9c61-93eaae070cf8 · outbound

This paper cites Generate, annotate, andlearn: Nlpwithsynthetictext.Transactions of the Association for Computational Linguistics, 10:826–842, 2022.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Generate, annotate, andlearn: Nlpwithsynthetictext.Transactions of the Association for Computational Linguistics, 10:826–842, 2022

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.451642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6585778f-995a-48d3-8041-87d183e2c601 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.442473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d7a4e2ba-8237-4bc2-9854-54f4daae699a · outbound

This paper cites GPT-4o System Card.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning GPT-4o System Card

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:16:55.258045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2270a882-0cf6-4c2e-ada6-c90d10a9b150 · outbound

This paper cites Mistral 7B.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Mistral 7B

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:16:55.261718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed913de2-4a81-4877-848d-6cd85a6152df · outbound

This paper cites Zeroth-order stochastic variance reduction for nonconvex optimization.Advances in neural information processing systems, 31, 2018.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Zeroth-order stochastic variance reduction for nonconvex optimization.Advances in neural information processing systems, 31, 2018

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.433301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fe3b78f3-8511-415a-9bb3-4705ad2f1ff3 · outbound

This paper cites Black, Adrian Weller, and Bernhard Schölkopf.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Black, Adrian Weller, and Bernhard Schölkopf

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.422336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c57c6def-1e5d-4868-93e5-e17efdd5b43a · outbound

This paper cites Winner- take-all column row sampling for memory efficient adaptation of language model.Advances in Neural Information Processing Systems, 36:3402–3424, 2023.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Winner- take-all column row sampling for memory efficient adaptation of language model.Advances in Neural Information Processing Systems, 36:3402–3424, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.412753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 265b6173-4459-493c-a52d-557817dc7259 · outbound

This paper cites On LLMs-driven synthetic data generation, curation, and evaluation: A survey.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning On LLMs-driven synthetic data generation, curation, and evaluation: A survey

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:16:55.273897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bc461b6-d4e3-4f62-aa19-3837761d8d13 · outbound

This paper cites Fine-tuning language models with just forward passes.Advances in Neural Information Processing Systems, 36:53038–53075, 2023.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Fine-tuning language models with just forward passes.Advances in Neural Information Processing Systems, 36:53038–53075, 2023

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.402801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8700f12f-f65d-4c49-9b1c-e7f7b3f32b62 · outbound

This paper cites Choice of plausible alterna- tives: An evaluation of commonsense causal reasoning.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Choice of plausible alterna- tives: An evaluation of commonsense causal reasoning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.393131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 27998da0-ea9c-4703-8ba8-ae54c32957e9 · outbound

This paper cites In defense of structural sparse adapters for concurrent llm serving.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning In defense of structural sparse adapters for concurrent llm serving

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.383959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 12e78dc6-eb3e-4eb4-99fd-eb3f9482a5af · outbound

This paper cites Glue: A multi-task benchmark and analysis platform for natural language understanding.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Glue: A multi-task benchmark and analysis platform for natural language understanding

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T05:16:55.284841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 343e21e0-3419-4f3a-b5be-83ac377d9678 · outbound

This paper cites Zeroth-Order Fine-Tuning of LLMs in Random Subspaces.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Zeroth-Order Fine-Tuning of LLMs in Random Subspaces

Reference 22

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:16:55.340252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 77682c96-2bfa-4b79-b7dd-bb033ceed912 · outbound

This paper cites Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, and Tianlong Chen.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, and Tianlong Chen

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:55.370084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 50c4f774-c729-4216-bf76-cfec8338833a · outbound

This paper cites A Survey on Data Augmentation in Large Model Era.

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning A Survey on Data Augmentation in Large Model Era

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:16:55.294429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 8ca090d3-cee8-4db1-a80d-c5edde7115be · inbound

GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations cites this paper.

GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:55:59.870666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0a5acafd-25a2-4b7a-b792-4a7196602a4c · inbound

GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations cites this paper.

GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-30T23:45:08.009788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.