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Paper Citation Record · LEDGER

LU-500: A Logo Benchmark for Concept Unlearning

As of 11 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2607.24101.

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

pith.paper-citation-record.v1
2607.24101 v2

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:06:27.554008Z

measured 66 of 66 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

66 of 66 outbound references displayed

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Outbound references

Observation c113f0f0-7e50-4555-86f4-7fffad65c1b9 · outbound

This paper cites Stable diffusion 2.0 release, 2023.

LU-500: A Logo Benchmark for Concept Unlearning Stable diffusion 2.0 release, 2023

Reference 1

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source=pdf_text observed=2026-07-31T23:06:18.690322Z digest=sha256:40201bbf950d2e0c6b75cecf6d77b29ad581e8605d119cfaed5f5220f592f630

Observation 7965d586-509e-42e9-87cd-a27a9cef74a1 · outbound

This paper cites Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models.

LU-500: A Logo Benchmark for Concept Unlearning Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models

Reference 2

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Observation fefcd839-3379-4da9-ac26-222a2177286c · outbound

This paper cites Sega: Instructing text-to-image models using semantic guidance.Advances in Neural Information Processing Systems, 36:25365–25389, 2023.

LU-500: A Logo Benchmark for Concept Unlearning Sega: Instructing text-to-image models using semantic guidance.Advances in Neural Information Processing Systems, 36:25365–25389, 2023

Reference 3

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Observation bbb64020-d790-4f65-9cbd-59bbd2faa34c · outbound

This paper cites Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient.

LU-500: A Logo Benchmark for Concept Unlearning Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient

Reference 4

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Observation 733ee8e4-4800-496d-8d65-e599f2ec57a9 · outbound

This paper cites Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts.

LU-500: A Logo Benchmark for Concept Unlearning Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts

Reference 5

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Observation 9ab6abdc-541c-4fee-92a5-a3540fafe80d · outbound

This paper cites A Survey of Machine Unlearning.

LU-500: A Logo Benchmark for Concept Unlearning A Survey of Machine Unlearning

Reference 6

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Observation cb8b1db9-5d81-4db1-85bb-e9e3e1fb1437 · outbound

This paper cites Avoiding Copyright Infringement via Large Language Model Unlearning.

LU-500: A Logo Benchmark for Concept Unlearning Avoiding Copyright Infringement via Large Language Model Unlearning

Reference 7

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Observation ae4f4346-4f45-43cc-8764-3177c38626c1 · outbound

This paper cites SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation.

LU-500: A Logo Benchmark for Concept Unlearning SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation

Reference 8

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Observation cd5a1e79-01b1-47b1-98b3-2e226b250e8f · outbound

This paper cites Copyright Traps for Large Language Models.

LU-500: A Logo Benchmark for Concept Unlearning Copyright Traps for Large Language Models

Reference 9

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Observation 4589139d-95f7-4f22-a409-8ea575f0ebc3 · outbound

This paper cites Six-CD: Benchmarking Concept Removals for Benign Text-to-image Diffusion Models.

LU-500: A Logo Benchmark for Concept Unlearning Six-CD: Benchmarking Concept Removals for Benign Text-to-image Diffusion Models

Reference 10

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Observation 0ee2b2fb-a6fe-44d8-963c-8ea83c1903b2 · outbound

This paper cites stable-diffusion-3-medium, 2024.

LU-500: A Logo Benchmark for Concept Unlearning stable-diffusion-3-medium, 2024

Reference 11

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Observation 7ea7a202-012a-44ad-a843-6d3b1fa59c62 · outbound

This paper cites Scaling rectified flow transform- ers for high-resolution image synthesis.

LU-500: A Logo Benchmark for Concept Unlearning Scaling rectified flow transform- ers for high-resolution image synthesis

Reference 12

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Observation 861268b2-12bd-4199-a6f1-be046c2374c6 · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

LU-500: A Logo Benchmark for Concept Unlearning High- resolution image synthesis with latent diffusion models

Reference 13

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Observation 56caed3a-b3ce-4de7-a81f-581f52aa970f · outbound

This paper cites Flux1.1 pro, 2024.

LU-500: A Logo Benchmark for Concept Unlearning Flux1.1 pro, 2024

Reference 14

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Observation 096e3d43-dce5-4b03-af68-21ec5476c8d5 · outbound

This paper cites Erasing concepts from diffusion models.

LU-500: A Logo Benchmark for Concept Unlearning Erasing concepts from diffusion models

Reference 15

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Observation 1818c34c-3508-4894-8451-af3769f6bd3c · outbound

This paper cites Forget-me- not: Learning to forget in text-to-image diffusion models.

LU-500: A Logo Benchmark for Concept Unlearning Forget-me- not: Learning to forget in text-to-image diffusion models

Reference 16

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source=pdf_text observed=2026-07-31T23:06:20.437751Z digest=sha256:8e9f8e3f5ca07153fec6e6d9830fd55ae0de6742792c74b5668c43db9f0bc030

Observation 6f2e92d8-7bba-4890-bd7f-b1df67de53c7 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4): 600–612, 2004.

LU-500: A Logo Benchmark for Concept Unlearning Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4): 600–612, 2004

Reference 17

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Observation 9a84d842-7207-4eb6-916d-83b6ac86f516 · outbound

This paper cites Logo-2k+: A large-scale logo dataset for scalable logo classification.

LU-500: A Logo Benchmark for Concept Unlearning Logo-2k+: A large-scale logo dataset for scalable logo classification

Reference 18

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Observation 58a155b2-4ab7-4f75-8584-4127878a325f · outbound

This paper cites Weblogo-2m: Scalable logo detection by deep learning from the web.

LU-500: A Logo Benchmark for Concept Unlearning Weblogo-2m: Scalable logo detection by deep learning from the web

Reference 19

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Observation 421936dd-9e65-424a-b76b-b44fd9ac9252 · outbound

This paper cites Logo retrieval with a contrario visual query expansion.

LU-500: A Logo Benchmark for Concept Unlearning Logo retrieval with a contrario visual query expansion

Reference 20

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Observation 9631a05d-75b6-445d-8e0a-88cfc00ad8e6 · outbound

This paper cites Scalable logo recognition in real-world images.

LU-500: A Logo Benchmark for Concept Unlearning Scalable logo recognition in real-world images

Reference 21

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source=pdf_text observed=2026-07-31T23:06:21.192758Z digest=sha256:92bc726fc1426d2fe1c47dd7f024a2345b8d65262323c1c545c9308c373a4513

Observation 01cfbaa5-bfc1-4834-958e-073884fad2cd · outbound

This paper cites A deep one-shot network for query-based logo retrieval.Pattern Recognition, 96:106965, 2019.

LU-500: A Logo Benchmark for Concept Unlearning A deep one-shot network for query-based logo retrieval.Pattern Recognition, 96:106965, 2019

Reference 22

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Observation 42661770-60f5-4463-9360-2e09c2d26cf6 · outbound

This paper cites The open brands dataset: Unified brand detection and recognition at scale.

LU-500: A Logo Benchmark for Concept Unlearning The open brands dataset: Unified brand detection and recognition at scale

Reference 23

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Observation 56f51af4-67bc-4df0-8374-b3166df87f17 · outbound

This paper cites Multi-scale vehicle logo detector.

LU-500: A Logo Benchmark for Concept Unlearning Multi-scale vehicle logo detector

Reference 24

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Observation 4c7465c0-233a-41dd-aa81-3cc2ec4cdb43 · outbound

This paper cites Foodlogodet-1500: A dataset for large-scale food logo detection via multi-scale feature decou- pling network.

LU-500: A Logo Benchmark for Concept Unlearning Foodlogodet-1500: A dataset for large-scale food logo detection via multi-scale feature decou- pling network

Reference 25

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Observation 3f84d0e7-2ad1-4f3f-abb0-b0a5d538adfa · outbound

This paper cites Seetek: Very large-scale open-set logo recognition with text-aware metric learning.

LU-500: A Logo Benchmark for Concept Unlearning Seetek: Very large-scale open-set logo recognition with text-aware metric learning

Reference 26

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Observation 1e290f4f-0206-440f-81d6-5f0fe1969f45 · outbound

This paper cites Deep learning for logo detection: A survey.ACM Transactions on Multimedia Computing, Communications and Applications, 20(3):1–23, 2023.

LU-500: A Logo Benchmark for Concept Unlearning Deep learning for logo detection: A survey.ACM Transactions on Multimedia Computing, Communications and Applications, 20(3):1–23, 2023

Reference 27

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Observation eef86fae-6194-4631-8bb1-9955b45be193 · outbound

This paper cites Logodet-3k: A large-scale image dataset for logo detection.ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 18(1):1–19, 2022.

LU-500: A Logo Benchmark for Concept Unlearning Logodet-3k: A large-scale image dataset for logo detection.ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 18(1):1–19, 2022

Reference 28

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Observation 05d8e7f5-52c0-43f5-bf62-2c3c99f2d1e4 · outbound

This paper cites Open Logo Detection Challenge.

LU-500: A Logo Benchmark for Concept Unlearning Open Logo Detection Challenge

Reference 29

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Observation 6a51cc0e-6882-44cb-a7ce-b81bc6d90dea · outbound

This paper cites LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks.

LU-500: A Logo Benchmark for Concept Unlearning LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks

Reference 30

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Observation 2dafc0b0-5293-4668-bff6-f47034d80d21 · outbound

This paper cites Open Set Logo Detection and Retrieval.

LU-500: A Logo Benchmark for Concept Unlearning Open Set Logo Detection and Retrieval

Reference 31

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Observation 7e340d34-0109-4f54-8694-61e7b9b2ccbb · outbound

This paper cites Comparative Analysis of Deep Learning Models for Brand Logo Classification in Real-World Scenarios.

LU-500: A Logo Benchmark for Concept Unlearning Comparative Analysis of Deep Learning Models for Brand Logo Classification in Real-World Scenarios

Reference 32

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Observation 906a4c08-b8f1-486c-82f0-664b8e40fcf2 · outbound

This paper cites Aligned Agents, Biased Swarm: Measuring Bias Amplification in Multi-Agent Systems.

LU-500: A Logo Benchmark for Concept Unlearning Aligned Agents, Biased Swarm: Measuring Bias Amplification in Multi-Agent Systems

Reference 33

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Observation 26157d1b-34db-4678-a28e-dcbe678d9f62 · outbound

This paper cites davinci-agency: Unlocking long-horizon agency data-efficiently.

LU-500: A Logo Benchmark for Concept Unlearning davinci-agency: Unlocking long-horizon agency data-efficiently

Reference 34

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Observation b4bcbbaf-096d-4b78-9c62-e84efdf4ea74 · outbound

This paper cites Kan-mixer: Kolmogorov-arnold networks for gene expression prediction in plant species.

LU-500: A Logo Benchmark for Concept Unlearning Kan-mixer: Kolmogorov-arnold networks for gene expression prediction in plant species

Reference 35

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Observation 8cb427ec-0987-4785-9443-358d0ecbf073 · outbound

This paper cites Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models.

LU-500: A Logo Benchmark for Concept Unlearning Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models

Reference 36

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Observation 0eb4b6c1-de19-4e33-b8f2-89ef7556e9fe · outbound

This paper cites One-dimensional adapter to rule them all: Concepts diffusion models and erasing applications.

LU-500: A Logo Benchmark for Concept Unlearning One-dimensional adapter to rule them all: Concepts diffusion models and erasing applications

Reference 37

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source=pdf_text observed=2026-07-31T23:06:23.454049Z digest=sha256:50da48fed0e4b1e35ad69a25b159b99000b98399b051896be063906702fd73c2

Observation a5d6e4ff-0559-486b-b0b5-f2fc782da8c9 · outbound

This paper cites Unified concept editing in diffusion models.

LU-500: A Logo Benchmark for Concept Unlearning Unified concept editing in diffusion models

Reference 38

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source=pdf_text observed=2026-07-31T23:06:23.602947Z digest=sha256:57cc138812f0c85e4dc83233e4df3f1d6eebec8027a58eb53f674ee60fae88d5

Observation 4eceac89-47e5-4325-b4f3-b6310421018b · outbound

This paper cites Mace: Mass concept erasure in diffusion models.

LU-500: A Logo Benchmark for Concept Unlearning Mace: Mass concept erasure in diffusion models

Reference 39

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source=pdf_text observed=2026-07-31T23:06:23.741954Z digest=sha256:66a15df375bc7fe6c26df4803642aa1bce34f0286b107e6e8d106c0c3cbb8de1

Observation 3f85f100-ca58-42cf-b362-7827d5f883a6 · outbound

This paper cites Editing massive concepts in text-to-image diffusion models.arXiv preprint arXiv:2403.13807, 2024.

LU-500: A Logo Benchmark for Concept Unlearning Editing massive concepts in text-to-image diffusion models.arXiv preprint arXiv:2403.13807, 2024

Reference 40

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source=pdf_text observed=2026-07-31T23:06:23.853925Z digest=sha256:6a28a9e12de97d668047be76dcb06779f756cf598f0e4b29100ba5261c4d866a

Observation 7d0046dd-9816-4447-a4a7-8abffad44dc1 · outbound

This paper cites A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models.

LU-500: A Logo Benchmark for Concept Unlearning A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models

Reference 41

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source=pdf_text observed=2026-07-31T23:06:24.013094Z digest=sha256:43e58d8c772a3f81ab71a9cf9fd7355d0dacf476f6b6c01dec73d4acef380428

Observation d99a8623-36a0-4eda-908d-071ea66352f5 · outbound

This paper cites UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models.

LU-500: A Logo Benchmark for Concept Unlearning UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models

Reference 42

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source=pdf_text observed=2026-07-31T23:06:24.140806Z digest=sha256:16a2ae39d3e65d5fd2f1d3ddf791e4a2fc858969ef648d2db33e25220a02b72c

Observation 6ab8d8f2-46bd-4532-8658-79ee79c3fb94 · outbound

This paper cites Ablating concepts in text-to-image diffusion models.

LU-500: A Logo Benchmark for Concept Unlearning Ablating concepts in text-to-image diffusion models

Reference 43

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source=pdf_text observed=2026-07-31T23:06:24.280825Z digest=sha256:b7888c82d7c4d6b30e415aba2bb4bcb6a0055dddc1947df122afc5ca34134630

Observation fc66c3bf-3e28-46b5-9d28-acac0cede4be · outbound

This paper cites Limi: Less is more for agency.arXiv preprint arXiv:2509.17567, 2025.

LU-500: A Logo Benchmark for Concept Unlearning Limi: Less is more for agency.arXiv preprint arXiv:2509.17567, 2025

Reference 44

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source=pdf_text observed=2026-07-31T23:06:24.432849Z digest=sha256:8c45d4d31b1264e5aacfbb337e07755b8a33e647557ae304bda231264d4ee278

Observation 18818ffc-e20f-461b-8285-4417b26a8024 · outbound

This paper cites DatasetResearch: Benchmarking Agent Systems for Demand-Driven Dataset Discovery.

LU-500: A Logo Benchmark for Concept Unlearning DatasetResearch: Benchmarking Agent Systems for Demand-Driven Dataset Discovery

Reference 45

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source=pdf_text observed=2026-07-31T23:06:24.572312Z digest=sha256:7587ef7c95aace7189380610eab69cc1a5a9f74f0bde9472067d14302a58d645

Observation e87e7e36-1b36-4d65-b35e-eed4dc64a240 · outbound

This paper cites AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts.

LU-500: A Logo Benchmark for Concept Unlearning AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts

Reference 46

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source=pdf_text observed=2026-07-31T23:06:24.702775Z digest=sha256:24ecdd85b6d3d89791a5e554e4c4cb3d71973c297825bd84c8bd8995cad59950

Observation ac88f77c-8038-491f-830c-459983d4213e · outbound

This paper cites Innovatorbench: Evaluating agents’ ability to conduct innovative llm research.arXiv preprint arXiv:2510.27598, 2025.

LU-500: A Logo Benchmark for Concept Unlearning Innovatorbench: Evaluating agents’ ability to conduct innovative llm research.arXiv preprint arXiv:2510.27598, 2025

Reference 47

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source=pdf_text observed=2026-07-31T23:06:24.869877Z digest=sha256:fcdb6e5881732877b1d0b8e219b1d2fb5840a54100372a876a1c073465741d0b

Observation fe8e3563-33ba-46fa-8bb1-75612095c16c · outbound

This paper cites Midjourney (V5.2) [Text-to-Image Model], 2023.

LU-500: A Logo Benchmark for Concept Unlearning Midjourney (V5.2) [Text-to-Image Model], 2023

Reference 48

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source=pdf_text observed=2026-07-31T23:06:25.031972Z digest=sha256:3a3974b4a6035db3863fc101be8ab8cc8d155b52b8a48bab69008b3ca57de48b

Observation f45c13af-b37e-4f55-95fa-083493a6e60b · outbound

This paper cites Dalle3, 2024.

LU-500: A Logo Benchmark for Concept Unlearning Dalle3, 2024

Reference 49

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source=pdf_text observed=2026-07-31T23:06:25.141378Z digest=sha256:addfb3f07c9563f3480e1bcd5c7a313c7bedee01064472f16a9bb4895e2f448e

Observation b5837851-3bc4-4fe3-aa8c-bb4a16e9d132 · outbound

This paper cites Scaling open-vocabulary object detection.Advances in Neural Information Processing Systems, 36, 2024.

LU-500: A Logo Benchmark for Concept Unlearning Scaling open-vocabulary object detection.Advances in Neural Information Processing Systems, 36, 2024

Reference 50

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source=pdf_text observed=2026-07-31T23:06:25.261806Z digest=sha256:b5cc98a2a8dc19ad0bedcb16c6afe5aa576e8ffd0842b5be204c5f8fbec38a00

Observation 0a9b3070-e49d-4bd8-b906-9c87a1a17801 · outbound

This paper cites Learning transferable visual models from natural language supervision.

LU-500: A Logo Benchmark for Concept Unlearning Learning transferable visual models from natural language supervision

Reference 51

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source=pdf_text observed=2026-07-31T23:06:25.427897Z digest=sha256:9c6cbe07056f205c5a7601e5b55fb1dab70f3902c2e759a04ebb7fcb6df23106

Observation eae90705-2932-44ff-8383-ea43649a2e67 · outbound

This paper cites stable-diffusion-v1-5, 2022.

LU-500: A Logo Benchmark for Concept Unlearning stable-diffusion-v1-5, 2022

Reference 52

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source=pdf_text observed=2026-07-31T23:06:25.545215Z digest=sha256:1535b8bf2ae4e29c8684d525243d9b95198f02bbd00f2ff9f7316f8ca16287fc

Observation 1be57c19-da89-4f75-8b13-7da0800415b3 · outbound

This paper cites A computational approach to edge detection, 1986.

LU-500: A Logo Benchmark for Concept Unlearning A computational approach to edge detection, 1986

Reference 53

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source=pdf_text observed=2026-07-31T23:06:25.670712Z digest=sha256:e0a05d6aeaa388e76b79461eb2686cab4daec5112706da66e4ba48f1fe67f57c

Observation 9597b58b-6c7f-4ed4-840e-823ab18443fb · outbound

This paper cites Texture characterization based on grey-level co-occurrence matrix.

LU-500: A Logo Benchmark for Concept Unlearning Texture characterization based on grey-level co-occurrence matrix

Reference 54

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source=pdf_text observed=2026-07-31T23:06:25.867094Z digest=sha256:2399c0249737c7bf0907e2312b4c9db1ff94598fa539a43ffb6c6b857dcfff8f

Observation 427a33b6-51c7-4eef-95b7-ba2617dc39fd · outbound

This paper cites Divergence measures based on the shannon entropy.IEEE Transactions on Information theory, 37(1):145–151, 1991.

LU-500: A Logo Benchmark for Concept Unlearning Divergence measures based on the shannon entropy.IEEE Transactions on Information theory, 37(1):145–151, 1991

Reference 55

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source=pdf_text observed=2026-07-31T23:06:26.031544Z digest=sha256:f82b95248c8ba3210b2f93e0e53b7a70c831a3d8a4c9d446b5368450ccb54bfe

Observation 74fff4d5-8ffc-4778-92fa-2f75466247e7 · outbound

This paper cites apple logo.

LU-500: A Logo Benchmark for Concept Unlearning apple logo

Reference 56

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source=pdf_text observed=2026-07-31T23:06:26.223079Z digest=sha256:8c031ee5df508e8510e5cf8e047c64b7c99605b362a46c45c8ed1fd00b8febcb

Observation 0b651a6f-7cca-4343-8baa-8181f2f84f7f · outbound

This paper cites an unresolved cited work.

LU-500: A Logo Benchmark for Concept Unlearning Unresolved cited work

Reference 57

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source=pdf_text observed=2026-07-31T23:06:26.384771Z digest=sha256:f37505a2cd7715fc6b07e93327d4cae99f736c4aff7ff5aa03edb0f4d13e1f38

Observation e7ca2795-eff7-4267-a1ae-a52c9656428f · outbound

This paper cites an unresolved cited work.

LU-500: A Logo Benchmark for Concept Unlearning Unresolved cited work

Reference 58

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source=pdf_text observed=2026-07-31T23:06:26.531946Z digest=sha256:fa4e326d6c7d33ba85bb2f7ff335c15e6f0f59809b9b082bcd25c4d0b31da073

Observation 684d3f14-f0c2-4428-aa07-6fe1c25f0aa1 · outbound

This paper cites an unresolved cited work.

LU-500: A Logo Benchmark for Concept Unlearning Unresolved cited work

Reference 59

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source=pdf_text observed=2026-07-31T23:06:26.719874Z digest=sha256:32cecdb947241b593131a90e27c78ab2a55c9dad924e19013d98c288de0dabf2

Observation e134c442-3622-42e7-9d43-a4509ae61913 · outbound

This paper cites Requirements: Generate 10 prompts directly in English, format them as follows:.

LU-500: A Logo Benchmark for Concept Unlearning Requirements: Generate 10 prompts directly in English, format them as follows:

Reference 60

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source=pdf_text observed=2026-07-31T23:06:26.870957Z digest=sha256:6ca6817869e3b79cb971e2196f6bf4a4b35f7a8587a2465626252f955e4045bb

Observation 1eece3f1-5954-4f8f-ab46-dd37b6dee4a4 · outbound

This paper cites Keep the prompts simple, without complex scenes.

LU-500: A Logo Benchmark for Concept Unlearning Keep the prompts simple, without complex scenes

Reference 61

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source=pdf_text observed=2026-07-31T23:06:27.001664Z digest=sha256:21c693e24dc1fdd0715099ec59a9f897bd34861c0d33893330b1524a1d458f65

Observation 7d478e43-dc88-41fa-8cb5-4574c68f622e · outbound

This paper cites an unresolved cited work.

LU-500: A Logo Benchmark for Concept Unlearning Unresolved cited work

Reference 62

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source=pdf_text observed=2026-07-31T23:06:27.090653Z digest=sha256:b25b16c11fc4a173f17a5cea2fdf96e8b1440076cddb7de931addbe3cd0f1474

Observation a52777a0-e7fe-4d14-bb78-7d5e3b28b656 · outbound

This paper cites an unresolved cited work.

LU-500: A Logo Benchmark for Concept Unlearning Unresolved cited work

Reference 63

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source=pdf_text observed=2026-07-31T23:06:27.221247Z digest=sha256:da9910e8c0c7acbc91a851f18974cd04fb3c6b3265c3d80e37af2840e685d19e

Observation 9e5cc5af-7f56-442b-bc7f-4089a165c32f · outbound

This paper cites an unresolved cited work.

LU-500: A Logo Benchmark for Concept Unlearning Unresolved cited work

Reference 64

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source=pdf_text observed=2026-07-31T23:06:27.284015Z digest=sha256:6ae4c0e7bdd2d44cc3cc17da79ce4a274b1a4bbeb840d1f5b8f77f7940c902ff

Observation a2023017-c606-4158-ba59-c325153befec · outbound

This paper cites Make the prompt as detailed as possible, but not overly lengthy.

LU-500: A Logo Benchmark for Concept Unlearning Make the prompt as detailed as possible, but not overly lengthy

Reference 65

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source=pdf_text observed=2026-07-31T23:06:27.438914Z digest=sha256:803e78df68021306ca1115ef79c9755550d1f5c35fd1191144aa09c2aaf7ea68

Observation a42039bd-3c56-4ace-9994-9d03ffc8c424 · outbound

This paper cites Explicit Implicit Figure 8: The agent prompts for generating LU-500 was crafted using OpenAI’s GPT-4o model.

LU-500: A Logo Benchmark for Concept Unlearning Explicit Implicit Figure 8: The agent prompts for generating LU-500 was crafted using OpenAI’s GPT-4o model

Reference 66

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source=pdf_text observed=2026-07-31T23:06:27.554008Z digest=sha256:56cb0eaf2b07cc8ca53c48e4501e42a1aa50d1ca62576d752c791bc85aa37ecf

Pith citing papers

No inbound Pith citation observations are available.