Paper Citation Record · LEDGER

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning

As of 23 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 8 inbound Pith citation observations for arXiv:2412.18693.

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

pith.paper-citation-record.v1
2412.18693 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:38:37.185167Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:23:48.057420Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T20:54:21.584820Z

Reference resolution

43 of 43 outbound references displayed

verified exact0
verified fuzzy6
unresolved37
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3a455212-b4e4-4735-a571-38a873193b0f · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Evaluating Large Language Models Trained on Code

Reference 1

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Observation 7607460b-5317-4a25-9a2f-eb068e8ca21c · outbound

This paper cites persuade the user to incorporate daily exercise for health benefits.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning persuade the user to incorporate daily exercise for health benefits

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:38:37.620528Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 58e53305-9bb9-4e0e-b728-82380d3faa7a · outbound

This paper cites Scalable Extraction of Training Data from (Production) Language Models.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Scalable Extraction of Training Data from (Production) Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.504371Z

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Observation 2e9c53fb-14f8-4ce6-b98f-412e6b863e3f · outbound

This paper cites Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.510827Z

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Observation e05c1f27-4a4c-4ccf-ac5c-bc5d8f27c3fd · outbound

This paper cites Aligning Large Multimodal Models with Factually Augmented RLHF.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Aligning Large Multimodal Models with Factually Augmented RLHF

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.570688Z

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Observation 982ef8cc-470e-4f0a-bd50-3f8e1e46ee80 · outbound

This paper cites FLIRT: Feedback Loop In-context Red Teaming.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning FLIRT: Feedback Loop In-context Red Teaming

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.687422Z

Source-reported events for the cited work

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Observation 939c8fbd-d949-46ea-936d-caa97c95d135 · outbound

This paper cites Gradient-Based Language Model Red Teaming.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Gradient-Based Language Model Red Teaming

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.691260Z

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Observation 1a297fb3-6279-4d64-883d-86c94f074646 · outbound

This paper cites PAL: Proxy-Guided Black-Box Attack on Large Language Models.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning PAL: Proxy-Guided Black-Box Attack on Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.695356Z

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Observation 05874896-e918-4dcf-8269-3d8d3d9a551e · outbound

This paper cites Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.699423Z

Source-reported events for the cited work

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Observation 73189aad-9820-43d7-8eda-0d60eb61fa66 · outbound

This paper cites Improving alignment of dialogue agents via targeted human judgements.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Improving alignment of dialogue agents via targeted human judgements

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.703622Z

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Observation d9f232a7-fddc-4604-947a-518bfb78ccda · outbound

This paper cites The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.707434Z

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Observation bc297b22-cd09-48cc-af46-d3c0fd9b0cb8 · outbound

This paper cites Towards evaluating the robustness of neural networks.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Towards evaluating the robustness of neural networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:38:37.686863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b5221044-d131-4726-b209-7bc3e89ce20e · outbound

This paper cites Gradient-based Adversarial Attacks against Text Transformers.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Gradient-based Adversarial Attacks against Text Transformers

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.757795Z

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Observation d15b9a1c-e6d5-4571-a6f5-9c82a6980dcf · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Categorical Reparameterization with Gumbel-Softmax

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.829242Z

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Observation 5185ca4f-084b-450b-8f0b-90d1ccc70238 · outbound

This paper cites Universal Adversarial Triggers for Attacking and Analyzing NLP.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.836773Z

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Observation 42180de9-4d5b-4b7d-bed6-6c96075f55ef · outbound

This paper cites Robust Conversational Agents against Imperceptible Toxicity Triggers.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Robust Conversational Agents against Imperceptible Toxicity Triggers

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.841457Z

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Observation eac0baf8-8037-4944-9c6b-671b07c2279e · outbound

This paper cites Automatically Auditing Large Language Models via Discrete Optimization.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Automatically Auditing Large Language Models via Discrete Optimization

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.845185Z

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Observation 2e0147cc-0a79-4dc0-953a-3688a585d55d · outbound

This paper cites Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.849025Z

Source-reported events for the cited work

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Observation 08739c8f-2184-4f18-bb4b-202f215b8875 · outbound

This paper cites Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.853362Z

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Observation 233c255d-e8ac-46c5-b10a-390fc426c1ca · outbound

This paper cites Adversarial Training for High-Stakes Reliability.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Adversarial Training for High-Stakes Reliability

Reference 28

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Observation d7042796-280e-4881-bb20-d605d86b1d02 · outbound

This paper cites Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.861024Z

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Observation 1166a25e-124b-40ba-97e6-955b28124f1f · outbound

This paper cites Dynabench: Rethinking benchmarking in NLP.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Dynabench: Rethinking benchmarking in NLP

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:38:37.641205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2cb2de59-09bb-4cb6-a524-6b0eadcbe133 · outbound

This paper cites doi: 10.18653/v1/2021.naacl-main.324.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning doi: 10.18653/v1/2021.naacl-main.324

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.939010Z

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Observation e5b8c7e7-81f2-4b82-aa35-284e15c98033 · outbound

This paper cites Explore, Establish, Exploit: Red Teaming Language Models from Scratch.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Explore, Establish, Exploit: Red Teaming Language Models from Scratch

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.990324Z

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Observation 75bd1147-8474-4be8-a15a-a528a8d57538 · outbound

This paper cites Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:37.043217Z

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Observation ca162ccd-40f3-4202-81b4-f32011e6a94f · outbound

This paper cites Curiosity-driven Red-teaming for Large Language Models.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Curiosity-driven Red-teaming for Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:37.047366Z

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Observation 1c403507-d700-42df-8865-88e97d87de29 · outbound

This paper cites MART: Improving LLM Safety with Multi-round Automatic Red-Teaming.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning MART: Improving LLM Safety with Multi-round Automatic Red-Teaming

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:37.051918Z

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Observation 42c4e0ae-1621-409c-8faf-9b0ea080fe6b · outbound

This paper cites Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:37.055923Z

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Observation 85991f10-4d48-445b-bea2-3e532e1bb01c · outbound

This paper cites Measuring and mitigating unintended bias in text classification.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Measuring and mitigating unintended bias in text classification

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9d3caa98-bb36-45a4-8ed2-e9c3daaa4868 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 38

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Observation aafab37d-06c4-4361-a6d1-f223b39fd5ad · outbound

This paper cites Text and Code Embeddings by Contrastive Pre-Training.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Text and Code Embeddings by Contrastive Pre-Training

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:37.067103Z

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Observation a8284c02-f3aa-4b32-908e-d09f7d04fed6 · outbound

This paper cites WebGPT: Browser-assisted question-answering with human feedback.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning WebGPT: Browser-assisted question-answering with human feedback

Reference 40

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Observation 814faea4-dd24-4b84-a8da-9fe04d1328ae · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning ReAct: Synergizing Reasoning and Acting in Language Models

Reference 41

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Observation 4320da0f-87a2-46ce-8a50-41aa3f79efdd · outbound

This paper cites safety jailbreak.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning safety jailbreak

Reference 43

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Source-reported events for the cited work

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Observation efa3be7c-51dd-4c59-99fb-dc2ea3882446 · outbound

This paper cites HotFlip: White-Box Adversarial Examples for Text Classification.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning HotFlip: White-Box Adversarial Examples for Text Classification

Reference 2016

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This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 2017

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Observation 807791ba-3657-485e-94cc-043103b873cd · outbound

This paper cites The Woman Worked as a Babysitter: On Biases in Language Generation.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning The Woman Worked as a Babysitter: On Biases in Language Generation

Reference 2018

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Observation 08db9907-3aa0-47c1-99be-94dc0272f2a4 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 2019

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Observation 6ee5f4db-4ee8-44d9-943b-c338d9c299e7 · outbound

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Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Red Teaming Language Models with Language Models

Reference 2020

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Observation cb77f118-3d69-4c1b-b2b7-67f426225bb9 · outbound

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Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning GPT-4 Technical Report

Reference 2021

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Observation 97724b42-e4b7-4616-a72b-0a0ba2a48971 · outbound

This paper cites Sander Schulhoff, Jeremy Pinto, Anaum Khan, Louis-Fran¸ cois Bouchard, Chenglei Si, Svetlina Anati, Valen Tagliabue, Anson Liu Kost, Christopher Carnahan, and Jordan Boyd-Graber.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Sander Schulhoff, Jeremy Pinto, Anaum Khan, Louis-Fran¸ cois Bouchard, Chenglei Si, Svetlina Anati, Valen Tagliabue, Anson Liu Kost, Christopher Carnahan, and Jordan Boyd-Graber

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:38:37.842335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e54732c8-b4b8-4a18-8a83-1a221d2700c8 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T04:38:36.490989Z

Source-reported events for the cited work

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Observation bfd78291-d084-4771-97dd-d87b2e086922 · outbound

This paper cites RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models.

Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

Reference 2024

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Pith citing papers

Observation 2b1b7a8a-c76d-4627-961e-6017443a229b · inbound

Jailbreaking to Jailbreak cites this paper.

Jailbreaking to Jailbreak Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning

Reference 7

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Observation a06ea541-bfea-4834-9e5e-d7489f9e8c11 · inbound

When Testing AI Tests Us: Safeguarding Mental Health on the Digital Frontlines cites this paper.

When Testing AI Tests Us: Safeguarding Mental Health on the Digital Frontlines Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T05:23:48.057420Z

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Observation a57cad7f-0a66-442d-ba67-07d698debdec · 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 Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning

Reference 2

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Observation ac7ef433-3e74-412b-8fe7-9589df932fb0 · inbound

Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models cites this paper.

Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:06.264820Z

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Observation 536888bb-9079-4d5f-ab57-719aa7f2a0f4 · inbound

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities cites this paper.

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:52:07.721429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f65c26c6-c0b8-4993-b6c5-7d009c0c4e55 · 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 Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:20.401969Z

Source-reported events for the cited work

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Observation 4861d50b-d0bd-417c-95cb-0513527f72a6 · inbound

Red-Bandit: Test-Time Adaptation for LLM Red-Teaming via Bandit-Guided LoRA Experts cites this paper.

Red-Bandit: Test-Time Adaptation for LLM Red-Teaming via Bandit-Guided LoRA Experts Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:54:21.586690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e8a800fe-adaf-4bc1-b6ff-5bf651c93279 · inbound

GPT-Red: Automated Red Teaming via Self-Play at Scale cites this paper.

GPT-Red: Automated Red Teaming via Self-Play at Scale Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning

Reference 2025

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