Paper Citation Record · LEDGER

Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2210.10683.

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

pith.paper-citation-record.v1
2210.10683 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:14:55.896568Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T04:09:34.710341Z

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 ddaa2197-671f-440c-b234-ec47304426f6 · inbound

A StrongREJECT for Empty Jailbreaks cites this paper.

A StrongREJECT for Empty Jailbreaks Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:28:02.786416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4f9fe4d0-2dfd-4328-bc38-f1508cbc812f · inbound

Extracting and Understanding the Superficial Knowledge in Alignment cites this paper.

Extracting and Understanding the Superficial Knowledge in Alignment Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-08T22:14:55.896568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e5107e8-9043-4d76-ba8e-cf5c28905560 · inbound

Evaluating Robustness of Large Audio Language Models to Audio Injection: An Empirical Study cites this paper.

Evaluating Robustness of Large Audio Language Models to Audio Injection: An Empirical Study Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:57.501242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbfdd1f4-5006-4668-ad4d-d7defadad16f · inbound

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models cites this paper.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T23:00:19.506346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6739bf55-838f-469f-aaa1-d46636b2ef52 · inbound

Anyone Can Jailbreak: Prompt-Based Attacks on LLMs and T2Is cites this paper.

Anyone Can Jailbreak: Prompt-Based Attacks on LLMs and T2Is Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T12:23:03.930809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f424507d-f92c-42ad-8c06-c8aec74d8042 · inbound

The First Differentiable Transfer-Based Algorithm for Discrete MicroLED Repair cites this paper.

The First Differentiable Transfer-Based Algorithm for Discrete MicroLED Repair Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-05T22:21:02.760268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcec3a58-277c-43ba-9c5e-8c99076fc256 · inbound

Harmful Prompt Laundering: Jailbreaking LLMs with Abductive Styles and Symbolic Encoding cites this paper.

Harmful Prompt Laundering: Jailbreaking LLMs with Abductive Styles and Symbolic Encoding Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:25.849173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 472464fd-c3a2-4519-919b-01ee2029bf4f · inbound

SafeSci: Safety Evaluation of Large Language Models in Science Domains and Beyond cites this paper.

SafeSci: Safety Evaluation of Large Language Models in Science Domains and Beyond Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:01:25.528515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 67369f55-d200-465b-89a9-5e1a35182b08 · inbound

FinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming cites this paper.

FinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:09:34.712003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e9066142-3e38-423f-a58b-e7b4417941e4 · inbound

An Empirical Evaluation of Prompt Injection Vulnerabilities in Large Language Models Across Multilingual and Obfuscated Attack Scenarios cites this paper.

An Empirical Evaluation of Prompt Injection Vulnerabilities in Large Language Models Across Multilingual and Obfuscated Attack Scenarios Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:54:20.376507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.