Paper Citation Record · LEDGER

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research

As of 8 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2509.10790.

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

pith.paper-citation-record.v1
2509.10790 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:38:24.890195Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

12 of 12 outbound references displayed

verified exact0
verified fuzzy0
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e89d8a8c-c2d8-4198-ac24-e725877ae245 · outbound

This paper cites Golden- eye: A platform for evaluating emerging numerical data formats in dnn accelerators,.

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research Golden- eye: A platform for evaluating emerging numerical data formats in dnn accelerators,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:24.210305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e90a888-f410-4fac-8086-f2dd4e049f6d · outbound

This paper cites Understanding error propagation in deep learning neural network (dnn) accelerators and applications,.

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research Understanding error propagation in deep learning neural network (dnn) accelerators and applications,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:24.252083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b7e1192-6f34-4097-b534-78eb61041a37 · outbound

This paper cites Tensorfi: A flexible fault injection framework for ten- sorflow applications,.

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research Tensorfi: A flexible fault injection framework for ten- sorflow applications,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:24.322995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d72f146e-41f8-42b7-9d3d-89dc14b93055 · outbound

This paper cites Tensorfi: A configurable fault injector for tensorflow applications,.

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research Tensorfi: A configurable fault injector for tensorflow applications,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:24.371320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7379194-27ea-4d53-994b-204f03a3c70e · outbound

This paper cites Fault injection for tensorflow applications,.

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research Fault injection for tensorflow applications,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:24.424175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72a808e8-9d48-4fdd-8d11-da6f25613cb8 · outbound

This paper cites Pytorchfi: A runtime perturbation tool for dnns,.

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research Pytorchfi: A runtime perturbation tool for dnns,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:24.471942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02f21f48-232f-4dd4-a10d-d1eb4ab0975a · outbound

This paper cites Binfi: an efficient fault injector for safety-critical machine learning systems,.

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research Binfi: an efficient fault injector for safety-critical machine learning systems,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:24.492554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7e92ad1-029a-45d4-b334-2615d2261f15 · outbound

This paper cites TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP.

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:24.527887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5fa965e-e20f-417d-9c15-285216b03b0f · outbound

This paper cites Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples.

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:24.621259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e58d185b-2f6c-470a-88fd-a1f506d52481 · outbound

This paper cites On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models.

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:24.722895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3dbdeeb-a240-4208-91d1-cfce273b7938 · outbound

This paper cites AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models.

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:24.816451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9a73c7e-38df-41dc-b61e-8c4c0cb7b544 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:24.890195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.