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Paper Citation Record · LEDGER

LADBench: A Benchmark for Logical Fault Detection in Images

As of 22 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2606.17433.

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

pith.paper-citation-record.v1
2606.17433 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T02:17:49.019550Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

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

measured 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

32 of 32 outbound references displayed

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  • verified fuzzy0
  • unresolved26
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation e7c88955-a72f-4db0-8d07-a3e645d6b3db · outbound

This paper cites OpenAI GPT-5 System Card.

LADBench: A Benchmark for Logical Fault Detection in Images OpenAI GPT-5 System Card

Reference 1

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local_arxiv, observed 2026-07-03T19:08:49.452425Z

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Observation 2352fad6-87d1-49a2-afc5-a399933be957 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

LADBench: A Benchmark for Logical Fault Detection in Images Gemini: A Family of Highly Capable Multimodal Models

Reference 2

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:9d9550484ef78dea0004a8391e6b69540e59f9beb7bd071c2c297674328fa5ae

Observation 6e71a3bb-398e-4b67-af80-64ccaddba30c · outbound

This paper cites Mmbench: Is your multi-modal model an all-around player?.

LADBench: A Benchmark for Logical Fault Detection in Images Mmbench: Is your multi-modal model an all-around player?

Reference 3

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:ba5cb3fe409d5029f8d952c976936f0f279540010d52a89531290583a6d70e9e

Observation 9518697d-fb1e-42b2-bc65-99c815a70e29 · outbound

This paper cites Logic unseen: Revealing the logical blindspots of vision-language models,.

LADBench: A Benchmark for Logical Fault Detection in Images Logic unseen: Revealing the logical blindspots of vision-language models,

Reference 4

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:26f5bb1b99f2df6f8baa7393579e81bec7cff203b7cfd5b557eb3733521ef84a

Observation 024aac2b-4c29-4c12-ad0d-1e380a56fe4f · outbound

This paper cites Logicqa: Logical anomaly detection with vision language model generated questions,.

LADBench: A Benchmark for Logical Fault Detection in Images Logicqa: Logical anomaly detection with vision language model generated questions,

Reference 5

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:fadaf78dd6ba937b5bd571cc68da59baeba7cf9475681ad4bd1986969238bc90

Observation 18e3e7d3-17eb-458d-a5bd-8c63d12939f0 · outbound

This paper cites Vision-language models can’t see the obvious,.

LADBench: A Benchmark for Logical Fault Detection in Images Vision-language models can’t see the obvious,

Reference 6

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:6cb6679b2f676966b5976a074cd35d1b5cc3d082ca2692069c54135ab2fcbb23

Observation 580bf83f-033b-4fb4-934d-ab4dd165aedb · outbound

This paper cites Embodied foundation models at the edge: A survey of deployment constraints and mitigation strategies.

LADBench: A Benchmark for Logical Fault Detection in Images Embodied foundation models at the edge: A survey of deployment constraints and mitigation strategies

Reference 7

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:10fe8e6e698c3e163e2f4722d0bda5cee0bf22d1fa6523aebf0686b9d6883483

Observation 56151c8b-6c55-48ae-a08d-06d93b8aca14 · outbound

This paper cites Spotting the unexpected (stu): A 3d lidar dataset for anomaly seg- mentation in autonomous driving,.

LADBench: A Benchmark for Logical Fault Detection in Images Spotting the unexpected (stu): A 3d lidar dataset for anomaly seg- mentation in autonomous driving,

Reference 8

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:bdec92e8d09e91d437bb35cf0e408a4ee76c355ff5e91f2107d02c3144417b77

Observation 64714918-5420-4d4b-af52-d24e86b8046b · outbound

This paper cites gpt-5-nano,.

LADBench: A Benchmark for Logical Fault Detection in Images gpt-5-nano,

Reference 9

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:ae9eaf21feb761595b23b59495f6f905ce26f2f8d7fe565f31bc6e6b5c66979a

Observation e32ae8f5-f3f0-46de-ad93-e1f3eecfa732 · outbound

This paper cites Plovad: Prompting vision- language models for open vocabulary video anomaly detection,.

LADBench: A Benchmark for Logical Fault Detection in Images Plovad: Prompting vision- language models for open vocabulary video anomaly detection,

Reference 10

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:081486f497e563b22211c0643530fb58edd05aca7e88265c6bd1d217ebee614e

Observation 3139b6cb-2882-49e0-829f-74c2cfef2601 · outbound

This paper cites Video anomaly detection in 10 years: A survey and outlook,.

LADBench: A Benchmark for Logical Fault Detection in Images Video anomaly detection in 10 years: A survey and outlook,

Reference 11

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:db1ef6f7212ce26924a05feb7ed3eeb61a4be5d84945e3c68e4135a06e4fb7e9

Observation 99169d5e-3807-49f6-8a7e-83fc13ee3dc8 · outbound

This paper cites Nesylad: A neuro-symbolic approach for unsupervised logi- cal anomaly detection,.

LADBench: A Benchmark for Logical Fault Detection in Images Nesylad: A neuro-symbolic approach for unsupervised logi- cal anomaly detection,

Reference 12

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:7aeceff61340a83b82b070649ed4df1e7c216cc1f7bdb033832e94d587128b5a

Observation f0548e74-f3f6-4908-aa3e-00d2ed815df1 · outbound

This paper cites Towards training-free anomaly detection with vision and language foundation models,.

LADBench: A Benchmark for Logical Fault Detection in Images Towards training-free anomaly detection with vision and language foundation models,

Reference 13

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:2c7f2ce1a52db14a7269a9941fd3ac440b6d87122bcaf05bbf48802be84a49b1

Observation 84e2162b-e41e-44bf-8b41-9120be79a244 · outbound

This paper cites Madclip: few-shot medical anomaly detection with clip,.

LADBench: A Benchmark for Logical Fault Detection in Images Madclip: few-shot medical anomaly detection with clip,

Reference 14

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:0e072fa7560f36611c3dcc3bbc14c9687182bcb511a08977337c00a5d3e3d128

Observation ef1b1475-44c8-458f-90be-70476f42d503 · outbound

This paper cites Adapting visual-language models for generalizable anomaly detection in medical images,.

LADBench: A Benchmark for Logical Fault Detection in Images Adapting visual-language models for generalizable anomaly detection in medical images,

Reference 15

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:b770b0d6894d4807ace40081c9f3f3f4ddfb5b82e04b7656698a929edf92225d

Observation 41da329b-f357-45b3-b1b1-9f8f27b2dc62 · outbound

This paper cites Smarthome-bench: A comprehensive benchmark for video anomaly detection in smart homes using multi-modal large language models,.

LADBench: A Benchmark for Logical Fault Detection in Images Smarthome-bench: A comprehensive benchmark for video anomaly detection in smart homes using multi-modal large language models,

Reference 16

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:a3eaf342d8018b8cb23a4cccf09955758c429295913a32bc7c24d7ae8d090b1e

Observation 2e567bae-7aed-42a5-9d77-8cfde723b73d · outbound

This paper cites Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detection,.

LADBench: A Benchmark for Logical Fault Detection in Images Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detection,

Reference 17

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:49a06a87db52651776730fd31baaed803e52d87bdd88846d044f752f285b97fc

Observation 8c9bfdc5-9621-401c-82cf-510865703ded · outbound

This paper cites Vane-bench: Video anomaly evaluation benchmark for conversational lmms,.

LADBench: A Benchmark for Logical Fault Detection in Images Vane-bench: Video anomaly evaluation benchmark for conversational lmms,

Reference 18

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:1a760bc2aca7a1e546941ec4d5a8eeba45a3f4cba6c801a907bd037272997a79

Observation 39e50aaa-6b8f-40cf-b0b1-5a9ac6c7a4fd · outbound

This paper cites Logicad: Explainable anomaly detection via vlm-based text feature extraction,.

LADBench: A Benchmark for Logical Fault Detection in Images Logicad: Explainable anomaly detection via vlm-based text feature extraction,

Reference 19

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:d2410497f571f1ea902a63a84e06c587be767b4a00737f9a0fc57cf087848640

Observation 6c85a7a3-8a7e-4921-a783-97385cda46f6 · outbound

This paper cites Gpt-4v-ad: Exploring grounding potential of vqa-oriented gpt- 4v for zero-shot anomaly detection,.

LADBench: A Benchmark for Logical Fault Detection in Images Gpt-4v-ad: Exploring grounding potential of vqa-oriented gpt- 4v for zero-shot anomaly detection,

Reference 20

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:c21c7504cc9a70ab912c7932cdfe956fa4159f5702f779ce11bd59109f27842e

Observation c85c487f-3064-4c53-875c-366fa315c325 · outbound

This paper cites Vbench: Comprehensive benchmark suite for video generative models,.

LADBench: A Benchmark for Logical Fault Detection in Images Vbench: Comprehensive benchmark suite for video generative models,

Reference 21

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:23df0c2c3b8936b9c8b5b3e7ab4d9346a8233bfe9aceb042884b8a5d42b19fdf

Observation f7022d8e-730d-435d-b8f6-b5eae83f4fae · outbound

This paper cites Multi-rag: A multimodal retrieval- augmented generation system for adaptive video understanding,.

LADBench: A Benchmark for Logical Fault Detection in Images Multi-rag: A multimodal retrieval- augmented generation system for adaptive video understanding,

Reference 22

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:bbea0f0c1a4ae8c3c79662361d5b0750a6352a6ee90a1fea31c868dcf703b424

Observation e77f55b4-1968-4786-a9ed-460eb3683ace · outbound

This paper cites Cave: Detecting and explaining commonsense anomalies in visual environments,.

LADBench: A Benchmark for Logical Fault Detection in Images Cave: Detecting and explaining commonsense anomalies in visual environments,

Reference 23

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:90f2c6d4edfbc0cac97b457e214ca100718234a0dab4bb9b3109eb6073eedd19

Observation 7bc9c847-16e8-4457-a7fb-c5f45f91b4dd · outbound

This paper cites FAM-Bench: A Multimodal Benchmark for Condition-Aware Food-as-Medicine Reasoning.

LADBench: A Benchmark for Logical Fault Detection in Images FAM-Bench: A Multimodal Benchmark for Condition-Aware Food-as-Medicine Reasoning

Reference 24

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:ce196b03049ce95c28f790be17c78383f0eac661168e6dd6cbd49b31d1d4066d

Observation ede7f848-765c-468d-a6f4-5ce1faad4303 · outbound

This paper cites gpt-image-1,.

LADBench: A Benchmark for Logical Fault Detection in Images gpt-image-1,

Reference 25

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Observation 09c33f6f-249e-40ad-be17-0d79d619206e · outbound

This paper cites gpt-5-mini,.

LADBench: A Benchmark for Logical Fault Detection in Images gpt-5-mini,

Reference 26

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Observation 8170f364-5ab1-4208-b5fa-26018fa8c427 · outbound

This paper cites [Online].

LADBench: A Benchmark for Logical Fault Detection in Images [Online]

Reference 27

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:a182f2278d18227c21e991b79efebdbc9c8206179e6a1a26a2d14b9155f2fdcb

Observation 36c4b20a-ecd4-4bd9-aab6-ac2c7b5dc88a · outbound

This paper cites claude-sonnet-4-6,.

LADBench: A Benchmark for Logical Fault Detection in Images claude-sonnet-4-6,

Reference 28

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:8b66cd8d9e0849c50badf9a53ddea4dcc0cf23a57d50e6e867799bbcd64633c9

Observation 5053256f-2367-483e-ac65-3c93ea7a9f20 · outbound

This paper cites gemini-3-flash-preview,.

LADBench: A Benchmark for Logical Fault Detection in Images gemini-3-flash-preview,

Reference 29

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:1b51937d018f1e24c68fefdde2f68312e24a9d7f3db87fb4ee0916f92f561fc7

Observation ab91e0cb-6928-4988-8a30-d5158ece00ae · outbound

This paper cites grok-4.1-fast-reasoning,.

LADBench: A Benchmark for Logical Fault Detection in Images grok-4.1-fast-reasoning,

Reference 30

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:8da938fd5ae1b4a4d8aad08ab17d67b43682b791fe4416c61763bcb1c1b11d2d

Observation 0f9c97cb-a115-4dd5-83c4-0e80a6003762 · outbound

This paper cites The Llama 3 Herd of Models.

LADBench: A Benchmark for Logical Fault Detection in Images The Llama 3 Herd of Models

Reference 31

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

source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:f7230c2435ea449bb0f5f003cc734f59af5457df064852281eced5a79fbac52f

Observation 03fb8593-2b9b-4203-9c38-f69e64f9143d · outbound

This paper cites Qwen3-VL Technical Report.

LADBench: A Benchmark for Logical Fault Detection in Images Qwen3-VL Technical Report

Reference 32

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source=pdf_text observed=2026-06-27T02:17:49.019550Z digest=sha256:fa9932c9dca56baceac1d3eed77395418eed3bc944d580efdb3255518a1b46a3

Pith citing papers

No inbound Pith citation observations are available.