Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning diverse attacks on large language models for robust red-teaming and safety tuning
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2405.18540.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T18:14:25.844619Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T15:41:17.036171Z
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 f015b579-a826-4ced-b02f-389e2d8872fc · inbound
Neural Genetic Search in Discrete Spaces cites this paper.
Neural Genetic Search in Discrete Spaces Learning diverse attacks on large language models for robust red-teaming and safety tuning
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T18:14:25.844619Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9691966f-846a-41ba-8fad-cb9078025d7d · inbound
Adversarial Preference Learning for Robust LLM Alignment cites this paper.
Adversarial Preference Learning for Robust LLM Alignment Learning diverse attacks on large language models for robust red-teaming and safety tuning
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:35:21.669202Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b7839bf-1144-4e59-9590-e49c9f2bbb38 · inbound
Jailbreak-R1: Exploring the Jailbreak Capabilities of LLMs via Reinforcement Learning cites this paper.
Jailbreak-R1: Exploring the Jailbreak Capabilities of LLMs via Reinforcement Learning Learning diverse attacks on large language models for robust red-teaming and safety tuning
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:01:08.855205Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 26fcdc41-bd95-4bb1-b0d9-aef4fda1bea2 · inbound
Watch, Listen, Understand, Mislead: Tri-modal Adversarial Attacks on Short Videos for Content Appropriateness Evaluation cites this paper.
Watch, Listen, Understand, Mislead: Tri-modal Adversarial Attacks on Short Videos for Content Appropriateness Evaluation Learning diverse attacks on large language models for robust red-teaming and safety tuning
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:09:22.585003Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a1881534-8996-4c2b-9edf-fed53cb73eb8 · inbound
From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models cites this paper.
From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Learning diverse attacks on large language models for robust red-teaming and safety tuning
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:45:20.466196Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e3620943-fba8-4cdf-8c73-9b0b3674bd14 · inbound
Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM cites this paper.
Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM Learning diverse attacks on large language models for robust red-teaming and safety tuning
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T23:13:03.820155Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eec2fb5a-a4f3-44fc-835b-60ebb1957c71 · inbound
Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation cites this paper.
Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation Learning diverse attacks on large language models for robust red-teaming and safety tuning
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:31:36.553261Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7c358abf-7bf5-4559-8649-380cf8d6e359 · inbound
LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems cites this paper.
LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems Learning diverse attacks on large language models for robust red-teaming and safety tuning
Reference 169
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T17:46:19.425971Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 305359b0-5c56-4033-a8ad-0c73cf638e2f · inbound
Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance cites this paper.
Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance Learning diverse attacks on large language models for robust red-teaming and safety tuning
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T15:41:17.115469Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.