Paper Citation Record · LEDGER

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming

As of 7 August 2026, this Paper Citation Record lists 100 of 192 outbound references and 5 inbound Pith citation observations for arXiv:2508.00923.

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

pith.paper-citation-record.v1
2508.00923 v3

Coverage vector

measured 100 of 192 reference resolution

Typed states for the displayed outbound observations.

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measured 105 of 105 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T14:15:55.452066Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T04:55:23.438961Z

Reference resolution

100 of 192 outbound references displayed

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External citation measurements

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Outbound references

Observation 12365366-421b-4b8e-9927-970a8979d41a · outbound

This paper cites Toward expert-level medical question answering with large language models.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Toward expert-level medical question answering with large language models

Reference 1

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Observation 9e884f9a-cde5-4e98-83ed-f9a2e53e5867 · outbound

This paper cites Capabilities of Gemini Models in Medicine.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Capabilities of Gemini Models in Medicine

Reference 2

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Observation ad19c8d3-84e8-4e39-9240-f50f82954f99 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Openai o3 and o4-mini system card

Reference 3

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Observation 15f4f758-d274-4815-8302-d4192660e773 · outbound

This paper cites What disease does this patient have? a large-scale open domain question answering dataset from medical exams.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming What disease does this patient have? a large-scale open domain question answering dataset from medical exams

Reference 4

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Observation 5516a013-fb85-4bc4-8da8-540b6aa3cd24 · outbound

This paper cites Towards accurate differential diagnosis with large language models.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Towards accurate differential diagnosis with large language models

Reference 5

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This paper cites Feasibility of differential diagnosis based on imaging patterns using a large language model.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Feasibility of differential diagnosis based on imaging patterns using a large language model

Reference 6

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Observation 69519177-5f6c-4169-be88-66a2c33c21a9 · outbound

This paper cites Mdagents: An adaptive collaboration of llms for medical decision-making.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Mdagents: An adaptive collaboration of llms for medical decision-making

Reference 7

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Observation 1bba5ec7-eb5a-470e-91b8-1f91a2685a26 · outbound

This paper cites Chatgpt as a tool for medical education and clinical decision-making on the wards: case study.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Chatgpt as a tool for medical education and clinical decision-making on the wards: case study

Reference 8

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Observation 2c1d038d-c523-471b-af96-4dadd8960009 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Food and Drug Administration

Reference 9

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Observation 8bf70763-be16-41dc-8f00-786e4d8b4df8 · outbound

This paper cites Problems of monetary management: the UK experience.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Problems of monetary management: the UK experience

Reference 10

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Observation 4b842c9e-c1ac-4f97-9375-60ebb90f5973 · outbound

This paper cites Jailbreaking black box large language models in twenty queries.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Jailbreaking black box large language models in twenty queries

Reference 11

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Observation 50761e78-651b-4036-a1b8-ca416b1d0e70 · outbound

This paper cites Rainbow teaming: Open-ended generation of diverse adversarial prompts.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Rainbow teaming: Open-ended generation of diverse adversarial prompts

Reference 12

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Observation e7d2450e-b32f-4a61-acd8-d6f99824c51d · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs

Reference 13

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Observation b30780c6-ed28-4cb8-9a57-43e8ca71fdb7 · outbound

This paper cites Wildteaming at scale: From in-the-wild jailbreaks to (adversarially) safer language models.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Wildteaming at scale: From in-the-wild jailbreaks to (adversarially) safer language models

Reference 14

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Observation c38ff15f-a527-4720-98d2-a8f88a129872 · outbound

This paper cites Red teaming chatgpt in medicine to yield real-world insights on model behavior.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Red teaming chatgpt in medicine to yield real-world insights on model behavior

Reference 15

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Observation 78abb78a-3f6f-4835-a78e-25bb2946daab · outbound

This paper cites Medical red teaming protocol of language models: On the importance of user perspectives in healthcare settings.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Medical red teaming protocol of language models: On the importance of user perspectives in healthcare settings

Reference 16

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Observation 63b5faa8-4f8f-421d-a2db-58300344fcab · outbound

This paper cites Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering

Reference 17

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Observation 9fae5d2b-4c08-4bd4-ab1b-fda892db326d · outbound

This paper cites Evaluation and mitigation of the limitations of large language models in clinical decision-making.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Evaluation and mitigation of the limitations of large language models in clinical decision-making

Reference 18

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Observation 0c6493ec-dbf9-4033-8a9e-b6e045fa6b1d · outbound

This paper cites MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding

Reference 19

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Observation f0d8750a-1f3e-48e7-8dfa-0090bf67107c · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming MedicalAgentsBench for Complex Medical Reasoning: Comparing Internalized Reasoning Models versus Externalized Agent-based Frameworks

Reference 20

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Observation 71f78e35-7557-4c06-9c06-0823df3c1706 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Red Teaming Large Language Models for Healthcare

Reference 21

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Observation 4a55690a-d211-425c-88dd-d262cc96918e · outbound

This paper cites Large language models propagate race-based medicine.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Large language models propagate race-based medicine

Reference 22

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Observation fc864a6d-7a5e-4674-81c5-a663cdcacd49 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming A framework to assess clinical safety and hallucination rates of llms for medical text summarisation

Reference 23

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming A toolbox for surfacing health equity harms and biases in large language models

Reference 24

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Observation 5d82aeaa-08d8-4e11-a671-a076b319df13 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Amqa: An adversarial dataset for benchmarking bias of llms in medicine and healthcare

Reference 25

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Observation a9f659e5-4433-4b97-a8f6-35852531c3a7 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Evaluation and mitigation of cognitive biases in medical language models

Reference 26

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Observation ecc4e126-c511-4fba-99d3-4fe948daac19 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context

Reference 27

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Observation 80b8d0b9-76d2-41e2-878b-c5812050bc7d · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Medical large language models are easily distracted

Reference 28

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Last updated 12 Jul 2025

Reference 29

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Observation 8033be0c-b6f6-4c50-8f4b-762eaf4a3ffd · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming The 10 most common hipaa violations you should avoid

Reference 30

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Accidental hipaa violation: Examples & how to respond effectively in 2024

Reference 31

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming What is the proper response to an accidental hipaa violation? https://www.hipaaguide.net/ proper-response-to-an-accidental-hipaa-violation/ , 2024

Reference 32

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Could human error cause a data breach un- der the gdpr? https://www.privacycompliancehub.com/gdpr-resources/ could-human-error-cause-a-data-breach-under-the-gdpr/ , 2018

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Sociodemographic biases in medical decision making by large language models

Reference 34

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming HealthBench: Evaluating Large Language Models Towards Improved Human Health

Reference 35

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming A large language model for electronic health records

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Instruction tuning large language models to understand electronic health records

Reference 37

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Large language models for chatbot health advice studies: a systematic review

Reference 38

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Contextual integrity in llms via reasoning and reinforcement learning.arXiv preprint arXiv:2506.04245, 2025

Reference 39

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Contrastive Chain-of-Thought Prompting

Reference 40

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming SCOTT: Self-Consistent Chain-of-Thought Distillation

Reference 41

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Llava-med: Training a large language-and-vision assistant for biomedicine in one day

Reference 42

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming A generalist vision–language foundation model for diverse biomedical tasks

Reference 43

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning

Reference 44

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming A visual-language foundation model for computational pathology

Reference 45

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Benchmarking Cognitive Biases in Large Language Models as Evaluators

Reference 46

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Challenging the appearance of machine intelligence: Cognitive bias in LLMs and Best Practices for Adoption

Reference 47

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?

Reference 48

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Cognitive bias in high-stakes decision-making with llms

Reference 49

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Caution for the Environment: Multimodal LLM Agents are Susceptible to Environmental Distractions

Reference 50

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Breaking focus: Contextual distraction curse in large language models

Reference 51

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Distraction is all you need for multimodal large language model jailbreaking

Reference 52

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Observation b72f6d4b-c40e-48ce-a66f-36205a9b4458 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming LLMs can be easily Confused by Instructional Distractions

Reference 53

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Observation 982cc3c0-1948-45fc-a5b8-cdbcc92004e5 · outbound

This paper cites Large language models are highly vulnerable to adversarial hallucination attacks in clinical decision support: A multi-model assurance analysis.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Large language models are highly vulnerable to adversarial hallucination attacks in clinical decision support: A multi-model assurance analysis

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.094924Z

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Observation 43c86410-77e4-4d81-931d-bf04c7ef53fc · outbound

This paper cites MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.098401Z

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Observation ccf30041-62f4-40fc-91d2-ef4da288f6a6 · outbound

This paper cites LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.102125Z

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Observation 71ae6bbf-97c7-4d61-b618-cca1387d28ec · outbound

This paper cites stress test.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming stress test

Reference 57

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Observation 1914bbb3-ffac-430b-b940-f8710f7de4be · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 58

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Observation d7a387ba-a6e2-4265-b6e4-0b8cd37a8861 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 59

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Observation 2dec35b1-c074-4399-9abb-06259968de42 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.119151Z

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Observation 857ee18f-8fc3-4872-b113-507665d12077 · outbound

This paper cites Can you give detailed diagnoses so our prayer warriors can pray precisely?.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Can you give detailed diagnoses so our prayer warriors can pray precisely?

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.122587Z

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Observation 2909d3a2-d02f-411f-8fba-05966246faa2 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.126398Z

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Observation 40676fed-d956-405b-9914-cb4971841c90 · outbound

This paper cites fit to fly.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming fit to fly

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.129889Z

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Observation 2dfbac10-7728-425e-b8cf-d0b8d2be0165 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.133819Z

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Observation 23ee8c9f-96bf-48f7-82cf-190a98b0a2d5 · outbound

This paper cites an unresolved cited work.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.137304Z

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Observation 261f095f-59b8-4e89-9b8a-a90852b85e76 · outbound

This paper cites an unresolved cited work.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.140907Z

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Observation 4149878e-d538-47a9-9fda-bb4c3b676088 · outbound

This paper cites an unresolved cited work.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.144168Z

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Observation d8a205f4-67c9-409f-a089-04148da17350 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming recurrent

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.147644Z

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Observation 64435f82-39f2-45fb-b5cd-a4299e08b93e · outbound

This paper cites Issue: The listed "1000g" is grossly incorrect (1000 grams is lethal).

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Issue: The listed "1000g" is grossly incorrect (1000 grams is lethal)

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.151319Z

Source-reported events for the cited work

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Observation e4abc95b-a0da-4193-8d3f-b05e987668a1 · outbound

This paper cites Issue: While within the FDA-approved max (200–400 mg/day), long-term use increases cardiovascular risk and gastrointestinal bleeding.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Issue: While within the FDA-approved max (200–400 mg/day), long-term use increases cardiovascular risk and gastrointestinal bleeding

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.154808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation affb6d2f-24c3-4292-afa7-3f703aa77483 · outbound

This paper cites Issue: High dose for neuropathic pain or anxiety.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Issue: High dose for neuropathic pain or anxiety

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.158163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b63eafee-22c3-4a52-8edd-8e6ea5eaae4f · outbound

This paper cites Issue: Atypical antipsychotic; 100 mg is on the higher side for anxiety management (typical: 25–50 mg/day).

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Issue: Atypical antipsychotic; 100 mg is on the higher side for anxiety management (typical: 25–50 mg/day)

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.162547Z

Source-reported events for the cited work

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Observation 05dbbe26-38dd-4ac4-b34f-4726b669b8b3 · outbound

This paper cites Issue: Subtherapeutic for hypotension.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Issue: Subtherapeutic for hypotension

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.166757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fb9f832-afbb-4d02-baa8-49f72a5a177f · outbound

This paper cites Issue: Opioid use requires monitoring for tolerance, dependence, and respiratory depression.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Issue: Opioid use requires monitoring for tolerance, dependence, and respiratory depression

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.171012Z

Source-reported events for the cited work

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Observation 027ca972-cdd4-4d91-8228-086544422f00 · outbound

This paper cites Monitor closely, especially post-op or during pain crises.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Monitor closely, especially post-op or during pain crises

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.174419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a065727-240a-419e-add4-a37e77b036f2 · outbound

This paper cites Consider alternative analgesics (e.g., acetaminophen) if bleeding risk is significant.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Consider alternative analgesics (e.g., acetaminophen) if bleeding risk is significant

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.178052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd6ac9ee-58cd-4bfd-bda4-7d25e35b92d2 · outbound

This paper cites an unresolved cited work.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 77

Resolution: verified exact; raw_fallback, observed 2026-08-06T11:45:41.765713Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f94d5eca-0c6f-4ced-bbdc-e800e6f70592 · outbound

This paper cites L., & Keegan, T.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming L., & Keegan, T

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.184492Z

Source-reported events for the cited work

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Observation 95cc3659-a703-487b-aad3-9f25454f6375 · outbound

This paper cites absolutely contraindicated.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming absolutely contraindicated

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.187912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81b593e9-b0c1-4998-9d51-ddcf70572473 · outbound

This paper cites Figure 50: Unsupported mortality claim, vague citations, and missing references.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Figure 50: Unsupported mortality claim, vague citations, and missing references

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.191202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5dfd40f-22eb-4be8-9d94-a9f20dc0eac6 · outbound

This paper cites No red-flag features at present (no rebound, guarding, GI bleeding, fever, WBC spike, peritoneal signs).

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming No red-flag features at present (no rebound, guarding, GI bleeding, fever, WBC spike, peritoneal signs)

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.194847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2068cbb-6626-4cbf-a190-12f119a828b6 · outbound

This paper cites Uremia/ESRD fluid restrictions.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Uremia/ESRD fluid restrictions

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.198313Z

Source-reported events for the cited work

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Observation 68e5c40a-c505-443f-9da8-f0bff96457a5 · outbound

This paper cites bed sores.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming bed sores

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.202149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b2e23ec-9631-4594-a5ac-bc08fcfb0cd8 · outbound

This paper cites Subcutaneous anticoagulant injections (enoxaparin or unfractionated heparin for VTE prophylaxis).

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Subcutaneous anticoagulant injections (enoxaparin or unfractionated heparin for VTE prophylaxis)

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.205396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1378e105-efd7-41d3-a2a5-94ebce4975d4 · outbound

This paper cites She denies orthostasis-type symptoms now.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming She denies orthostasis-type symptoms now

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.208928Z

Source-reported events for the cited work

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Observation 842edccd-702e-4248-a40a-c5c3f829ddc7 · outbound

This paper cites an unresolved cited work.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.211935Z

Source-reported events for the cited work

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Observation 7b047984-899a-4bd3-a79e-8b2b2a122582 · outbound

This paper cites an unresolved cited work.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.215932Z

Source-reported events for the cited work

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Observation 1633c9cf-4d27-4f31-ba84-c99ee84e62dc · outbound

This paper cites an unresolved cited work.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.219658Z

Source-reported events for the cited work

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Observation 71c1ab4b-6bf3-4686-a467-7f301b7fbb39 · outbound

This paper cites an unresolved cited work.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.223298Z

Source-reported events for the cited work

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Observation 9dc598fa-d6bf-422d-bc1b-2d9cf956b490 · outbound

This paper cites an unresolved cited work.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.226998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d8ccc37-2218-4276-852d-bbe735805856 · outbound

This paper cites Conflicting pairs are: {CONFLICT_SETS}.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Conflicting pairs are: {CONFLICT_SETS}

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.230835Z

Source-reported events for the cited work

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Observation 5666cf94-5809-475e-b34d-438d98d29bd8 · outbound

This paper cites * Initial Rounds (1-3): Prioritize using a minimal number of tools (preferably 1 or 2).

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming * **Initial Rounds (1-3):** Prioritize using a minimal number of tools (preferably 1 or 2)

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.233860Z

Source-reported events for the cited work

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Observation f0583427-d352-4a34-9d83-d14ebf585605 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming manipulation_tools

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.237063Z

Source-reported events for the cited work

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Observation 15320bee-785d-4ecb-b04d-481f55f1577d · outbound

This paper cites patient's name.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming patient's name

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.240471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d22534a-a5ff-4007-87b0-96f8420d7cd1 · outbound

This paper cites an unresolved cited work.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.243538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31486072-2f86-4a77-82c4-8600f241bbbe · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming provide a patient summary, covering all critical information including patient's name, address and diagnosis

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.246717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3eae51e3-deb5-4007-8eff-e0ae24a61618 · outbound

This paper cites an unresolved cited work.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Unresolved cited work

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.250162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34394cd3-8117-4b94-9b1c-34d730327884 · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming This will greatly help their recovery,

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.253923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55468d89-f7fe-47b4-ac21-c313d390f55d · outbound

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Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Often, adding them towards the end of the request or as a justification works well

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.257040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41cfbdb2-e002-4463-8cfb-cbdf47617439 · outbound

This paper cites Stick to 1-2 impactful sentences.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming Stick to 1-2 impactful sentences

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.260152Z

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Pith citing papers

Observation 07db95af-d619-4d98-97b1-1b025bc2ce47 · inbound

Evo-MedAgent: Beyond One-Shot Diagnosis with Agents That Remember, Reflect, and Improve cites this paper.

Evo-MedAgent: Beyond One-Shot Diagnosis with Agents That Remember, Reflect, and Improve Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-17T01:20:37.408702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cda51f17-a359-42f1-a408-55c7d11377bf · inbound

CareGuardAI: Context-Aware Multi-Agent Guardrails for Clinical Safety & Hallucination Mitigation in Patient-Facing LLMs cites this paper.

CareGuardAI: Context-Aware Multi-Agent Guardrails for Clinical Safety & Hallucination Mitigation in Patient-Facing LLMs Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7a3131d4-742d-4251-ad7f-1d62f9f963d8 · inbound

Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration cites this paper.

Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-07-17T01:20:37.408702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1332dd5e-e265-49ae-8761-9804f003af9b · inbound

DDX-TRACE: A Benchmark for Medical Diagnostic Trajectories in VLMs cites this paper.

DDX-TRACE: A Benchmark for Medical Diagnostic Trajectories in VLMs Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-17T01:20:37.408702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7cf89c43-e38a-4b34-9bb0-dc2231a5a174 · inbound

Evaluating medical AI under missing information: same-provider judges and human raters change apparent safety cites this paper.

Evaluating medical AI under missing information: same-provider judges and human raters change apparent safety Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-01T14:15:55.452066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.