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Paper Citation Record · LEDGER

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding

As of 19 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2507.21507.

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

pith.paper-citation-record.v1
2507.21507 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:46:19.590250Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ac12060c-6718-4695-bedc-93e5dc3fcfc1 · outbound

This paper cites A revisit of sparse coding based anomaly detection in stacked rnn framework,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding A revisit of sparse coding based anomaly detection in stacked rnn framework,

Reference 1

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 5a8b79ec-0323-412a-b236-0717a3b63f83 · outbound

This paper cites Few-shot scene-adaptive anomaly detection,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Few-shot scene-adaptive anomaly detection,

Reference 2

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Observation 18aeca26-8405-48b8-a8be-9e8d7ca20ea2 · outbound

This paper cites Fastano: Fast anomaly de- tection via spatio-temporal patch transformation,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Fastano: Fast anomaly de- tection via spatio-temporal patch transformation,

Reference 3

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Observation 04046219-d9d5-48de-b35e-bd51785d3161 · outbound

This paper cites Improved anomaly detection in surveillance videos with multiple probabilistic models inference,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Improved anomaly detection in surveillance videos with multiple probabilistic models inference,

Reference 4

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 1b22a331-538c-4092-9ce5-a41989811fba · outbound

This paper cites Novel applications for vae-based anomaly detection systems,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Novel applications for vae-based anomaly detection systems,

Reference 5

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Observation 82ce177e-ef5a-44af-964f-a258dd10653e · outbound

This paper cites Making reconstruction-based method great again for video anomaly detection,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Making reconstruction-based method great again for video anomaly detection,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation a975ff9a-9b8d-47de-94a9-e26dae670d17 · outbound

This paper cites A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction,

Reference 7

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Observation 39e091db-f781-4076-80d3-64a9030b0320 · outbound

This paper cites Video anomaly detection by solving decoupled spatio-temporal jigsaw puz- zles,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Video anomaly detection by solving decoupled spatio-temporal jigsaw puz- zles,

Reference 8

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Observation 554243e5-94e0-44c2-8cea-db98cf105c51 · outbound

This paper cites Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection

Reference 9

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Observation 1a3b3bff-03f7-45dc-b857-0d2efce11947 · outbound

This paper cites Generative cooperative learning for unsupervised video anomaly detection,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Generative cooperative learning for unsupervised video anomaly detection,

Reference 10

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Observation 40f0cde2-439e-4b44-b2fb-57ba8d8be48d · outbound

This paper cites Clip-tsa: Clip-assisted temporal self-attention for weakly-supervised video anomaly detection,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Clip-tsa: Clip-assisted temporal self-attention for weakly-supervised video anomaly detection,

Reference 11

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Observation 299a4172-19c4-47f2-aeb5-75bf12ab6a03 · outbound

This paper cites VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection

Reference 12

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local_arxiv, observed 2026-08-06T12:46:19.921853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 39df8caf-0576-4e03-8206-934cb341be41 · outbound

This paper cites Catching both gray and black swans: Open-set supervised anomaly detection,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Catching both gray and black swans: Open-set supervised anomaly detection,

Reference 13

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Observation 61fa1ca9-b828-42dd-b865-915229987ebb · outbound

This paper cites Towards open set video anomaly detection,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Towards open set video anomaly detection,

Reference 14

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 4566ac5b-eaa7-482f-966f-a8f25cd6222f · outbound

This paper cites Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection

Reference 15

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local_arxiv, observed 2026-08-06T12:46:19.898284Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 58a65e5c-e619-401a-aa0b-fbe55d3b5257 · outbound

This paper cites Hawk: Learning to understand open-world video anomalies,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Hawk: Learning to understand open-world video anomalies,

Reference 16

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Observation 6aee25f6-b75d-424d-9a2c-0abb9f796861 · outbound

This paper cites VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models

Reference 17

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation b5e45a4b-3d81-4071-bb94-13d2861335c3 · outbound

This paper cites VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs

Reference 18

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Observation 093cad5b-8c9d-4dc3-b713-689261d3ca22 · outbound

This paper cites Suvad: Semantic understanding based video anomaly detection using mllm,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Suvad: Semantic understanding based video anomaly detection using mllm,

Reference 19

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Observation 8209b336-85d1-48b2-86a6-c1f27909158c · outbound

This paper cites Harness- ing large language models for training-free video anomaly detection,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Harness- ing large language models for training-free video anomaly detection,

Reference 20

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ba845819-6f83-4568-b80d-cbc6615c2846 · outbound

This paper cites Anyanomaly: Zero-shot customizable video anomaly detection with lvlm,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Anyanomaly: Zero-shot customizable video anomaly detection with lvlm,

Reference 21

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Observation 9539f4a0-c7cd-4201-8999-c33910401d0f · outbound

This paper cites Video Anomaly Detection and Explanation via Large Language Models.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Video Anomaly Detection and Explanation via Large Language Models

Reference 22

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Observation aae62810-4675-4b8d-b9c8-e977376942d1 · outbound

This paper cites Scene-adaptive svad based on multi- modal action-based feature extraction,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Scene-adaptive svad based on multi- modal action-based feature extraction,

Reference 23

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 5e197aa8-cec0-4542-a957-dd78bfdee563 · outbound

This paper cites Uncovering what why and how: A comprehensive bench- mark for causation understanding of video anomaly,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Uncovering what why and how: A comprehensive bench- mark for causation understanding of video anomaly,

Reference 24

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 7b7ea1c3-6087-4195-86ac-7339836a56ec · outbound

This paper cites Video-LLaVA: Learning United Visual Representation by Alignment Before Projection.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

Reference 25

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Observation e27b375b-5317-4540-b42d-d6fe98846f67 · outbound

This paper cites Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding

Reference 26

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Observation 1be65b63-89a6-412e-a69d-a2328243b8cd · outbound

This paper cites Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models

Reference 27

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Observation 8d54d34d-1ca0-4c07-aa58-5a99f6d09b43 · outbound

This paper cites Vtimellm: Empower llm to grasp video moments,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Vtimellm: Empower llm to grasp video moments,

Reference 28

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation fa0a3282-54b3-4d91-bdec-8d477e67ba6a · outbound

This paper cites mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

Reference 29

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Unavailable: canonical work link unavailable.

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Observation 6f64ac80-4c6c-4f06-9083-e5928a15529d · outbound

This paper cites Region-aware pretraining for open- vocabulary object detection with vision transformers,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Region-aware pretraining for open- vocabulary object detection with vision transformers,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 19044621-7c8a-463f-a97c-bfe626fec1cc · outbound

This paper cites Dual discriminator generative adver- sarial network for video anomaly detection,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Dual discriminator generative adver- sarial network for video anomaly detection,

Reference 31

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation b9fc6f55-8248-4f78-93d8-20f207322a55 · outbound

This paper cites Spatio- temporal autoencoder for video anomaly detection,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Spatio- temporal autoencoder for video anomaly detection,

Reference 32

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation a7550118-fd99-440e-84f0-e4d2ad878f2e · outbound

This paper cites Synthetic temporal anomaly guided end-to-end video anomaly detection,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Synthetic temporal anomaly guided end-to-end video anomaly detection,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 9f9a2346-78cc-44b4-bfeb-44368b1d9590 · outbound

This paper cites Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T12:46:20.121786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation a817c356-0f1b-4827-a21c-d647c39dc5f3 · outbound

This paper cites Gods: Generalized one-class discriminative subspaces for anomaly detection,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Gods: Generalized one-class discriminative subspaces for anomaly detection,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T12:46:20.104214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 1823a973-2d84-45b6-ac03-885c3d56a176 · outbound

This paper cites A deep one-class neural network for anomalous event detection in complex scenes,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding A deep one-class neural network for anomalous event detection in complex scenes,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T12:46:20.089408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T12:46:19.549681Z digest=sha256:f3c19b3aa1d8ae19ef9b5555a1e092ae9c75681df12e79c5061e109c843f1803

Observation 650b361d-5862-48cf-a780-896c9b9fc585 · outbound

This paper cites Ubnormal: New benchmark for supervised open-set video anomaly detection,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Ubnormal: New benchmark for supervised open-set video anomaly detection,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T12:46:20.072715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T12:46:19.553951Z digest=sha256:30e55c78f63af07e954af815cecfbdf552ac500a24e4ccb5e4a4ccd9bc0d2c94

Observation 5633eb77-bab4-47a9-93a7-aba0e2a1c60e · outbound

This paper cites Follow the rules: reasoning for video anomaly detection with large language models,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Follow the rules: reasoning for video anomaly detection with large language models,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T12:46:20.054809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T12:46:19.558247Z digest=sha256:3ab54d2c7aaa3519eb6c5c9a0270324e8452bccb7c0f8f8becfe2bb1699c2ab4

Observation 224307fc-6724-453f-b374-c1d7b610c401 · outbound

This paper cites Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T12:46:19.562834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:46:19.562834Z digest=sha256:d92bc7daf16bb185bad01fdfa600c9e75b2931ee027fa0e13feef43f83ab9ead

Observation ef02caf3-3a76-4263-a7e3-e1e24f8721cb · outbound

This paper cites Real-world anomaly detection in surveillance videos,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Real-world anomaly detection in surveillance videos,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T12:46:20.037031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T12:46:19.567552Z digest=sha256:3609bd6d9bcc4f49966dd2c8561c762cd04ee2ae5798687a19f2f17fc30a008e

Observation e6bbbf2a-fb6d-4080-9687-20946354e797 · outbound

This paper cites Not only look, but also listen: Learning multimodal violence detection under weak supervision,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Not only look, but also listen: Learning multimodal violence detection under weak supervision,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T12:46:20.020135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T12:46:19.572158Z digest=sha256:3ec0a764fa2188ec1669633f17b2df8eef491e82b56a604840d6b42ff03ed529

Observation 2fa4d571-fdfd-4c2a-a79e-4e56d0137b2b · outbound

This paper cites an unresolved cited work.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-06T12:46:20.002913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T12:46:19.576850Z digest=sha256:bdbfd9eaf44075c339c096014d354741b02a8fb171068ca07555ea87f5f15f2f

Observation ae7d06d2-0912-4def-9079-052ebd045f2f · outbound

This paper cites Rouge: A package for automatic evaluation of sum- maries,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Rouge: A package for automatic evaluation of sum- maries,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T12:46:19.985779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T12:46:19.581316Z digest=sha256:225a1d13698b89d01c4432317e9c30d327d9666225e4809d4b2123bfdbfceaf0

Observation d6209e0d-38b5-40d3-b08f-b3bfa45a5150 · outbound

This paper cites Bleu: a method for automatic evaluation of machine translation,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Bleu: a method for automatic evaluation of machine translation,

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T12:46:19.585712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:46:19.585712Z digest=sha256:78286b7d6bdb97e69963869c8d60bf7c2296cd5165213102d934b4f52419d3f6

Observation 2e1f3604-6bdc-4e9b-878e-5ab3bc32fa62 · outbound

This paper cites Meteor: An automatic metric for mt evalua- tion with improved correlation with human judgments,.

VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding Meteor: An automatic metric for mt evalua- tion with improved correlation with human judgments,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T12:46:19.958557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T12:46:19.590250Z digest=sha256:6c69816fea0408e2588de1168ba61086073d138dfb00a1d4fb620a47221477db

Pith citing papers

No inbound Pith citation observations are available.