Paper Citation Record · LEDGER

MIRIAD: Augmenting LLMs with millions of medical query-response pairs

As of 14 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2506.06091.

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

pith.paper-citation-record.v1
2506.06091 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:04:43.802360Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T07:05:40.821282Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

50 of 50 outbound references displayed

verified exact0
verified fuzzy25
unresolved24
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation d7c777d9-73b5-4c83-adbc-16a1237d5675 · outbound

This paper cites Gpt-4 technical report.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Gpt-4 technical report

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.635323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d042c2e7-eb6f-4362-a605-39a92e3ef98e · outbound

This paper cites MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.588790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f3f381f-ca91-4a2c-b3ab-395369507e1f · outbound

This paper cites Large language models in machine translation.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Large language models in machine translation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.620655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fd7fe0b5-09fb-494d-bd82-c4a464b5a4ea · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.606241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e49c8075-3e34-49bf-96fe-9a826b311564 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.604392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46d3fade-060b-441e-ab95-be3ef49430d2 · outbound

This paper cites The Llama 3 Herd of Models.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs The Llama 3 Herd of Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.609072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fe3c69e-2f16-4092-88f5-ee5ab956ebc2 · outbound

This paper cites Jina-colbert-v2: A general-purpose multilingual late interaction retriever.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Jina-colbert-v2: A general-purpose multilingual late interaction retriever

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.582512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d65b1055-8eec-4abb-8986-3186d42610aa · outbound

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

MIRIAD: Augmenting LLMs with millions of medical query-response pairs What disease does this patient have? a large-scale open domain question answering dataset from medical exams

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.618690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6aa1fd24-6d1e-48be-b7eb-b0f80132cca0 · outbound

This paper cites Pubmedqa: A dataset for biomedical research question answering.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Pubmedqa: A dataset for biomedical research question answering

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.622789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e58d2672-cba0-492e-85c1-b811d7d3538b · outbound

This paper cites Colbert: Efficient and effective passage search via contextualized late interaction over bert.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Colbert: Efficient and effective passage search via contextualized late interaction over bert

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.627029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dab62534-cef1-42b6-853f-b58a85cd269e · outbound

This paper cites Medexqa: Medical question answering benchmark with multiple explanations.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Medexqa: Medical question answering benchmark with multiple explanations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.537868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9471b9af-f52a-40b9-858b-093354c19ba1 · outbound

This paper cites Bioasq-qa: A manually curated corpus for biomedical question answering.Scientific Data, 10(1):170, 2023.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Bioasq-qa: A manually curated corpus for biomedical question answering.Scientific Data, 10(1):170, 2023

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.522634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dd50041e-379e-4068-992a-5d89c3361a72 · outbound

This paper cites Halueval: A large-scale hallucination evaluation benchmark for large language models.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Halueval: A large-scale hallucination evaluation benchmark for large language models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.640074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6fb5744-8768-47ee-9f1d-402603265bfc · outbound

This paper cites Retrieval augmented generation or long-context llms? a comprehensive study and hybrid approach.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Retrieval augmented generation or long-context llms? a comprehensive study and hybrid approach

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.497449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a84ac0b8-4ea2-49c6-b09a-f6f5f452204d · outbound

This paper cites Can large language models reason about medical questions?Patterns, 5(3), 2024.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Can large language models reason about medical questions?Patterns, 5(3), 2024

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.482639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c0d36719-932b-49ce-bca7-4bfedea32764 · outbound

This paper cites DeepSeek-V3 Technical Report.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs DeepSeek-V3 Technical Report

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.653070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55d0ad95-15e8-4a60-89fa-de5e212c478a · outbound

This paper cites Query rewriting via large language models.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Query rewriting via large language models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.657625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a8e12e3-28e2-49b9-867d-3265b02b0acb · outbound

This paper cites S2orc: The semantic scholar open research corpus.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs S2orc: The semantic scholar open research corpus

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.467600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 57a56f97-0f57-4418-8a28-d22b32178b47 · outbound

This paper cites RaFe: Ranking Feedback Improves Query Rewriting for RAG.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs RaFe: Ranking Feedback Improves Query Rewriting for RAG

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.666531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 942caf96-6456-41c7-92d2-b9fa7658f263 · outbound

This paper cites Med-flamingo: a multimodal medical few-shot learner.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Med-flamingo: a multimodal medical few-shot learner

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.671207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a02713ed-9d11-4c6e-b88b-10f3ee21b653 · outbound

This paper cites Hybrid retrieval-augmented generation approach for llms query response enhancement.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Hybrid retrieval-augmented generation approach for llms query response enhancement

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.441857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7c2dc4a5-4506-44b8-8d34-0f82d5cd5526 · outbound

This paper cites Openbiollms: Advancing open-source large lan- guage models for healthcare and life sciences.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Openbiollms: Advancing open-source large lan- guage models for healthcare and life sciences

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.427191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 40b1d7c2-5cdb-4767-9ac9-b867d95a19a5 · outbound

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

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Medmcqa: A large-scale multi- subject multi-choice dataset for medical domain question answering

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.412212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a813a628-08d9-4f50-a315-19107788858e · outbound

This paper cites Med-halt: Medical domain hallucination test for large language models.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Med-halt: Medical domain hallucination test for large language models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.397810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1bf45452-7e9b-4caa-ae04-f2dc4c3af7cf · outbound

This paper cites Medhallu: A comprehensive benchmark for detecting medical hallucinations in large language models.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Medhallu: A comprehensive benchmark for detecting medical hallucinations in large language models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.383053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2a16df9e-1ab8-4ccf-890b-e08e40401804 · outbound

This paper cites The fineweb datasets: Decanting the web for the finest text data at scale.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs The fineweb datasets: Decanting the web for the finest text data at scale

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.703353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1e7a82c-cff3-467f-91db-7fd56e400bda · outbound

This paper cites Qdrant - vector database.https://qdrant.tech/.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Qdrant - vector database.https://qdrant.tech/

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.359844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 88e99828-d665-41fa-bb64-d48be942fd26 · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.346226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5263b2ea-586a-4513-a6d6-b0821d3192f9 · outbound

This paper cites Colbertv2: Effective and efficient retrieval via lightweight late interaction.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Colbertv2: Effective and efficient retrieval via lightweight late interaction

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.332606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 55d6d91f-8f72-4866-a6bd-b59bb7d1ec74 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs LLaMA: Open and Efficient Foundation Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.720972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57a15689-e1d6-46ce-9211-6e145a12a4aa · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.725330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b88a8d7f-3f54-4e15-b5d9-257bef1f5c68 · outbound

This paper cites Minilm: Deep self- attention distillation for task-agnostic compression of pre-trained transformers.Advances in neural information processing systems, 33:5776–5788, 2020.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Minilm: Deep self- attention distillation for task-agnostic compression of pre-trained transformers.Advances in neural information processing systems, 33:5776–5788, 2020

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.729883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22fa3658-dee2-44ee-b988-f82d27b96b17 · outbound

This paper cites Pmc-llama: to- ward building open-source language models for medicine.Journal of the American Medical Informatics Association, 31(9):1833–1843, 2024.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Pmc-llama: to- ward building open-source language models for medicine.Journal of the American Medical Informatics Association, 31(9):1833–1843, 2024

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.309317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 30ebd207-4525-4d5e-8387-5adc4116e80d · outbound

This paper cites C-pack: Packed resources for general chinese embeddings.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs C-pack: Packed resources for general chinese embeddings

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.738956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ab9a0ca-e001-418d-bc58-a8e3137cf534 · outbound

This paper cites Benchmarking retrieval-augmented generation for medicine.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Benchmarking retrieval-augmented generation for medicine

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.284780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3a1182cd-44b8-473c-b37a-dad959a2ca0e · outbound

This paper cites Medicine.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Medicine

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.270992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 06b52c33-2af3-46fe-b4f7-237f02335f14 · outbound

This paper cites an unresolved cited work.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-07T06:04:44.256483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1f4ee02d-0e8f-4118-ae02-101bd28b6ddd · outbound

This paper cites Streamlit.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Streamlit

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.242966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6258872c-d816-4858-a76b-d569a1aecfa1 · outbound

This paper cites bge-base-en-v1.5.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs bge-base-en-v1.5

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.229154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f3bbd178-59b0-4433-8585-1e7d896551b7 · outbound

This paper cites 28 Prompt for Quality Control - Relevance Check (Cont’d).

MIRIAD: Augmenting LLMs with millions of medical query-response pairs 28 Prompt for Quality Control - Relevance Check (Cont’d)

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.215190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 50b15b54-a60b-4ab3-881a-d951745d0b75 · outbound

This paper cites an unresolved cited work.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-07T06:04:44.200269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a7e793b0-509c-49fd-91f0-8d5986289e99 · outbound

This paper cites good" or.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs good" or

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.186128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b1b40de9-2ace-4354-8299-a50261c6d70e · outbound

This paper cites Provide a short explanation for your classification.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Provide a short explanation for your classification

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:44.171034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f1aa1a7f-dfce-4145-8fce-95fea7d670a7 · outbound

This paper cites an unresolved cited work.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-07T06:04:44.154565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5658cc73-be62-4ed8-875f-7649922d9df4 · outbound

This paper cites an unresolved cited work.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-07T06:04:44.140574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 51a523d2-5497-4c68-9c56-34cb609c6f1d · outbound

This paper cites an unresolved cited work.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-07T06:04:44.125428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d6d2733a-2043-4430-82b3-141e2940bbb1 · outbound

This paper cites an unresolved cited work.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-07T06:04:44.110892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 33aba751-5964-436a-bcc8-d9efadda2407 · outbound

This paper cites The” or “In.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs The” or “In

Reference 50

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T06:04:44.096323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 41ab1746-4269-47ee-927c-04dfd90b0ebc · outbound

This paper cites GPT-4 Technical Report.

MIRIAD: Augmenting LLMs with millions of medical query-response pairs GPT-4 Technical Report

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.584030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dee28d99-0f05-47a3-ab70-c6ddcfb66ee1 · outbound

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

MIRIAD: Augmenting LLMs with millions of medical query-response pairs MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:43.698234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 793e4c2b-727c-48d5-a345-2f21c6fb7ea6 · inbound

Hijacking Agent Memory: Stealthy Trojan Attacks Through Conversational Interaction cites this paper.

Hijacking Agent Memory: Stealthy Trojan Attacks Through Conversational Interaction MIRIAD: Augmenting LLMs with millions of medical query-response pairs

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-06-29T07:13:15.820189Z

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