Paper Citation Record · LEDGER

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs

As of 10 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 5 inbound Pith citation observations for arXiv:2501.19057.

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

pith.paper-citation-record.v1
2501.19057 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:33:30.093168Z

measured 33 of 33 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:06:18.054020Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T15:31:07.435202Z

Reference resolution

28 of 28 outbound references displayed

verified exact0
verified fuzzy3
unresolved25
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 37c9dac1-a9df-4f84-a81b-22411b115a2b · outbound

This paper cites GPT-4 Technical Report.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.614447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a392b37b-dbf3-4b3e-b37d-23f5a52e592d · outbound

This paper cites an unresolved cited work.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-09T21:33:30.730854Z

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 f3b1108a-fa77-490d-9165-e92add4bd2c5 · outbound

This paper cites ZO + LoRA.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs ZO + LoRA

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:33:30.629623Z

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 f9e535d5-52e1-4202-ab13-08472644b29d · outbound

This paper cites From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.691119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 953dcdae-7052-4a88-9231-dc8af2294d65 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.694502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43a1b4bd-2e40-4d08-bd2f-47e90e9e521c · outbound

This paper cites A Comprehensive Overview of Large Language Models.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs A Comprehensive Overview of Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.729053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6711d623-5ac6-4f2d-98df-6ca046d81f3c · outbound

This paper cites On Efficient Training of Large-Scale Deep Learning Models: A Literature Review.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs On Efficient Training of Large-Scale Deep Learning Models: A Literature Review

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.788730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b413ee0-05a8-4422-85be-6607946e4d1a · outbound

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

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs LLaMA: Open and Efficient Foundation Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.807136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73958aff-8adf-4c61-ba80-eee884317792 · outbound

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

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs Zeroth-Order Fine-Tuning of LLMs in Random Subspaces

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.901364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3964bce-b2ba-4ac8-9667-256c32ae6de6 · outbound

This paper cites K., Oh, S., and He, N.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs K., Oh, S., and He, N

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:33:30.745458Z

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 922908ae-4ab8-45ff-94c5-824be826362a · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs OPT: Open Pre-trained Transformer Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.941969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f55e5e3a-2a9a-48f3-a6ac-9b12f734a8f2 · outbound

This paper cites HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.961755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2aeda7c-3bfc-494a-b488-8d76c3da4aaa · outbound

This paper cites A Survey of Large Language Models.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs A Survey of Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.995561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd6650d0-2de1-43c6-aa1a-b4f5098474cb · outbound

This paper cites Second-Order Fine-Tuning without Pain for LLMs:A Hessian Informed Zeroth-Order Optimizer.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs Second-Order Fine-Tuning without Pain for LLMs:A Hessian Informed Zeroth-Order Optimizer

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:30.030277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05851746-bd3e-4952-92bf-3f91782398d2 · outbound

This paper cites an unresolved cited work.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-09T21:33:30.675210Z

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 fd74af2f-9efc-45ad-9be5-1cce1f619df6 · outbound

This paper cites an unresolved cited work.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-09T21:33:30.510580Z

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 2a20f2dd-b8ba-45ec-b25f-38d5aef364fb · outbound

This paper cites an unresolved cited work.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-09T21:33:30.720341Z

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 de792326-7972-4776-b07a-bca62e471e7c · outbound

This paper cites Sparse mezo: Less parameters for better perfor- mance in zeroth-order llm fine-tuning.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs Sparse mezo: Less parameters for better perfor- mance in zeroth-order llm fine-tuning

Reference 1907

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.716938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3fbf735-fe8f-4f0f-a111-8320468c58ed · outbound

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

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs LoRA: Low-Rank Adaptation of Large Language Models

Reference 1927

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.687381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f02e258c-f7d6-4e0f-8237-7ac1590461ca · outbound

This paper cites Gradientless Descent: High-Dimensional Zeroth-Order Optimization.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs Gradientless Descent: High-Dimensional Zeroth-Order Optimization

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.668630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5dba745c-cd70-4cb3-9093-0be6233c4e50 · outbound

This paper cites Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank Structures.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank Structures

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.660302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21751fd3-c14c-4165-84a2-0ba114215e92 · outbound

This paper cites AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.859093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c67d2fc4-2c9c-40ba-96fc-fee69aa6759a · outbound

This paper cites Simultaneous Computation and Memory Efficient Zeroth-Order Optimizer for Fine-Tuning Large Language Models.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs Simultaneous Computation and Memory Efficient Zeroth-Order Optimizer for Fine-Tuning Large Language Models

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.834494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f213c424-941a-4efc-8de9-9b3a3693ee38 · outbound

This paper cites DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.653469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01b75a96-6839-42fb-853d-8b41f819b58f · outbound

This paper cites Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.677207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c7f7c78-a58c-4485-bbb4-846b836dc43f · outbound

This paper cites Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.663460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07a0d409-25ac-45cd-8f31-74881165d264 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-09T21:33:29.632646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f90b6b36-dc3c-4e43-9218-24dd992963eb · outbound

This paper cites On the inherent privacy of two point zeroth order projected gradient de- scent.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs On the inherent privacy of two point zeroth order projected gradient de- scent

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:33:30.760733Z

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.

Pith citing papers

Observation 6e76a80a-b0e6-44fe-ac7a-e0d00ebef3bd · inbound

FZOO: Fast Zeroth-Order Optimizer for Fine-Tuning Large Language Models towards Adam-Scale Speed cites this paper.

FZOO: Fast Zeroth-Order Optimizer for Fine-Tuning Large Language Models towards Adam-Scale Speed TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:06:18.054020Z

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Unavailable: canonical work link unavailable.

Observation 15714c68-19ad-4a56-b90b-b95a89e545e4 · inbound

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs cites this paper.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:49.295868Z

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Unavailable: canonical work link unavailable.

Observation 0f15c1d8-e7f1-420c-90fc-5eacd80242f0 · inbound

Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations cites this paper.

Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T10:51:22.306073Z

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 e498798a-305d-4609-b58c-923de490b436 · inbound

Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling cites this paper.

Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:30:19.657864Z

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 62f7d2df-c49f-449e-a436-b18cb61dd784 · inbound

AdaMeZO: Adam-style Zeroth-Order Optimizer for LLM Fine-tuning Without Maintaining the Moments cites this paper.

AdaMeZO: Adam-style Zeroth-Order Optimizer for LLM Fine-tuning Without Maintaining the Moments TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:31:07.441349Z

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.