Paper Citation Record · LEDGER

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

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 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 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:28:49.295868Z

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

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

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

Source-reported events for the cited work

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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+00:00.