Paper Citation Record · LEDGER

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection

As of 8 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2508.07819.

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

pith.paper-citation-record.v1
2508.07819 v6

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:53:06.846339Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:54:08.172846Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T21:54:10.676871Z

Reference resolution

26 of 26 outbound references displayed

verified exact2
verified fuzzy7
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8f0a963e-c5b5-43db-85ee-eacaa71c6abd · outbound

This paper cites GPT-4 Technical Report.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:04.030137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e3d237b-234e-4f26-91cc-d3b24aeaaacd · outbound

This paper cites Must read: A systematic survey of computational persuasion.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Must read: A systematic survey of computational persuasion

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:04.444024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae089a55-cfdf-4e19-923b-2b32e050913e · outbound

This paper cites Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:53:07.443008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d6a21e58-e494-499c-828d-c41cecc57b2d · outbound

This paper cites ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:04.675671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b6ef0f9-136e-41d7-ad95-6e09d5b2ae20 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Evaluating Large Language Models Trained on Code

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:04.790094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23ddf141-20ed-4687-b293-e06ac2165db4 · outbound

This paper cites Improving Factuality and Reasoning in Language Models through Multiagent Debate.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Improving Factuality and Reasoning in Language Models through Multiagent Debate

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:04.968765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17e5d398-fa67-43c2-9e36-bd16d35fc907 · outbound

This paper cites Measuring the persuasiveness of language models,.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Measuring the persuasiveness of language models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:53:09.354129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 60a15cbf-7b4b-4718-bc7a-e873bcd761bb · outbound

This paper cites Large Language Model based Multi-Agents: A Survey of Progress and Challenges.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Large Language Model based Multi-Agents: A Survey of Progress and Challenges

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:05.315952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7ab6cf2-618d-4f7b-b82e-3e98872463d4 · outbound

This paper cites AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:05.427263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df0a0365-f228-492a-9b9e-5756553c93b2 · outbound

This paper cites Coda-19: Using a non-expert crowd to annotate research aspects on 10,000+ abstracts in the covid-19 open research dataset.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Coda-19: Using a non-expert crowd to annotate research aspects on 10,000+ abstracts in the covid-19 open research dataset

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:53:08.913859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ef0e7375-c4ea-4f06-8a22-b310009cfd3c · outbound

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

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Pubmedqa: A dataset for biomedical research question answering

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:53:08.744828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 137a4a91-b8be-4813-8208-36246c9d8b27 · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Self-refine: Iterative refinement with self-feedback

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:53:08.384785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 52969f0d-2fa7-456c-a3df-6b7a5f8aafee · outbound

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

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:06.007369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 893d18cb-abf9-426d-b1c1-8b8b0d118420 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:06.078321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8d8f783-aacc-414c-adae-3464c4a33db7 · outbound

This paper cites Towards Expert-Level Medical Question Answering with Large Language Models.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Towards Expert-Level Medical Question Answering with Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:06.140014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7332f556-fb7b-4d98-8c80-e1639ca2cc40 · outbound

This paper cites Are Expert-Level Language Models Expert-Level Annotators?.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Are Expert-Level Language Models Expert-Level Annotators?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:06.230654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76b94dd9-df7b-45f0-93f8-e27988fb705f · outbound

This paper cites LLMaAA: Making Large Language Models as Active Annotators.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection LLMaAA: Making Large Language Models as Active Annotators

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:06.330433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 059462a8-3551-4d08-95a8-254fcfc0bc70 · outbound

This paper cites Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:06.494073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95f5c21d-e59a-4512-8cca-476ff0630eb7 · outbound

This paper cites In this task, annotators are tasked to label each segment as background, purpose, method, finding/contribution, or other sections.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection In this task, annotators are tasked to label each segment as background, purpose, method, finding/contribution, or other sections

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:53:08.104579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d5004961-16d8-40fa-9303-1e8297b010dc · outbound

This paper cites 15 Published as a conference paper at COLM 2025 Figure 6: The annotation guideline of FOMC dataset.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection 15 Published as a conference paper at COLM 2025 Figure 6: The annotation guideline of FOMC dataset

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:53:07.793571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d01c06f0-8f65-4d5a-b0be-bf1142bee453 · outbound

This paper cites CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:05.540866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f93063ba-2456-4b4a-acc7-40853cfda174 · outbound

This paper cites GPTs Are Multilingual Annotators for Sequence Generation Tasks.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection GPTs Are Multilingual Annotators for Sequence Generation Tasks

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:53:07.166862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation df8a9536-e894-41a6-bfb9-028b88bd36ca · outbound

This paper cites Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:05.813591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db92829f-04e8-4e15-9388-a6100d99a3d8 · outbound

This paper cites Open-Source LLMs for Text Annotation: A Practical Guide for Model Setting and Fine-Tuning.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Open-Source LLMs for Text Annotation: A Practical Guide for Model Setting and Fine-Tuning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:04.125439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fd78740-71ec-44ba-840d-c5fdfeb2f6f6 · outbound

This paper cites Neel Guha, Julian Nyarko, Daniel Ho, Christopher R´e, Adam Chilton, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel Rockmore, Diego Zambrano, et al.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Neel Guha, Julian Nyarko, Daniel Ho, Christopher R´e, Adam Chilton, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel Rockmore, Diego Zambrano, et al

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:53:09.163009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6214ee55-0263-40dd-8ada-baa598cc5a41 · outbound

This paper cites Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:04.275918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation a11da4c9-92ba-4acc-ad4a-f09a07fc098a · inbound

Deep Learning-Based Desikan-Killiany Parcellation of the Brain Using Diffusion MRI cites this paper.

Deep Learning-Based Desikan-Killiany Parcellation of the Brain Using Diffusion MRI ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T21:54:10.784392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.