Pith. sign in

Paper Citation Record · LEDGER

AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2311.08592.

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

pith.paper-citation-record.v1
2311.08592 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:36:29.929432Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T13:17:39.532550Z

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 6936c644-67ee-477b-addf-217c0ccaf4c7 · inbound

ShieldGemma: Generative AI Content Moderation Based on Gemma cites this paper.

ShieldGemma: Generative AI Content Moderation Based on Gemma AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-20T13:17:39.534477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-20T13:17:39.444002Z digest=sha256:f0d331e236296e3be8e448fda2f4247e93bc2b57d41403a25858fc57a3bd118e

Observation b3decd66-e4ce-4d58-b058-5e42f68b9e61 · inbound

WeAudit: Scaffolding User Auditors and AI Practitioners in Auditing Generative AI cites this paper.

WeAudit: Scaffolding User Auditors and AI Practitioners in Auditing Generative AI AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Reference 100

Resolution
unresolved
no resolver link, observed 2026-08-10T22:31:35.867286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:31:35.867286Z digest=sha256:606eda2344137e8e19a0852a4cf24a9ce220dcca0060c6d4afa1242a3ab0933c

Observation 0ad69ee1-6bb9-4756-bbd3-c8bb60047b0a · inbound

LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch cites this paper.

LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Reference 109

Resolution
unresolved
no resolver link, observed 2026-08-10T20:54:02.356798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T20:54:02.356798Z digest=sha256:f2ffaf8614c10958f9abe3adf086dea36c9c8d652e5d311af0081345b8addff8

Observation 0ed834d0-fcf5-4753-8705-d0b41d4f463d · inbound

Aegis2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails cites this paper.

Aegis2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-10T20:15:54.785900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T20:15:54.785900Z digest=sha256:bc42872f72799b802c14f543f2fd7e80c5a5cba7fb9ddbbb96263adaca48e926

Observation 28a9226e-e311-4f31-9fe9-3b5ae73cbed8 · inbound

The Aloe Family Recipe for Open and Specialized Healthcare LLMs cites this paper.

The Aloe Family Recipe for Open and Specialized Healthcare LLMs AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-15T23:36:29.929432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:36:29.929432Z digest=sha256:1e89d2ff897c951f27408ed1a126891ee47d6628c8a00d9dc955169ec3a006bb

Observation e953e225-a21a-4603-b694-b50fb4963a10 · 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 AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T14:45:20.513666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:45:20.513666Z digest=sha256:4ab1323ff83444e4a476f8327d706c3f4b8851338c6d1a04a104516ca3b87ec0

Observation b2ae3156-39d9-4a0b-80cc-0eebe06135f7 · inbound

Agentic Web: Weaving the Next Web with AI Agents cites this paper.

Agentic Web: Weaving the Next Web with AI Agents AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Reference 180

Resolution
unresolved
no resolver link, observed 2026-08-06T13:05:39.839795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T13:05:39.839795Z digest=sha256:7e360dc989ed3774654b6352ee2979a74919ee3045858b2dd0425306abc9b9dc

Observation f4093c14-bee1-4cca-9516-90ccde7d5b27 · inbound

Libra: Large Chinese-based Safeguard for AI Content cites this paper.

Libra: Large Chinese-based Safeguard for AI Content AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T12:17:38.700638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:17:38.700638Z digest=sha256:a858fabc3d45d0737f2a714fb83d09779c22b21417a94d46bacf71c51b1bcafb

Observation faef68d6-738a-401f-94cd-4d9999757189 · inbound

Learning to Conceal Risk: Controllable Multi-turn Red Teaming for LLMs in the Financial Domain cites this paper.

Learning to Conceal Risk: Controllable Multi-turn Red Teaming for LLMs in the Financial Domain AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-18T17:51:41.870219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-18T17:49:42.112564Z digest=sha256:1b6beadcac610c47a4ada06c45ae0edc1ed0375ec5a84b8556ac616c4efc7ca9

Observation d84c92da-2222-42a2-8752-5f263b3c9b38 · inbound

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B cites this paper.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-02T14:52:12.574580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T14:52:12.574580Z digest=sha256:ef7dd9e11b1070726071409fdf2eeaba570fdac31780f2c3b2205651988ca8e6

Observation 153fcb74-d969-4170-8d67-9e08ce1803b1 · inbound

Adversarial Stress Testing of Role-Playing Language Agents using Multi-Agent Evaluation cites this paper.

Adversarial Stress Testing of Role-Playing Language Agents using Multi-Agent Evaluation AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T00:29:56.208408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:29:56.208408Z digest=sha256:ce83bd44dadb5a2c3b5d4b4bdbb0bbbda9698a0c253b0a78b379a45d3347136a