Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-31T23:06:27.554008Z
Paper Citation Record · LEDGER
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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-31T23:06:27.554008Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
66 of 66 outbound references displayed
External citation measurements
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Observation c113f0f0-7e50-4555-86f4-7fffad65c1b9 · outbound
LU-500: A Logo Benchmark for Concept Unlearning Stable diffusion 2.0 release, 2023
Reference 1
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Observation 7965d586-509e-42e9-87cd-a27a9cef74a1 · outbound
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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Observation 6f2e92d8-7bba-4890-bd7f-b1df67de53c7 · outbound
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
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
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
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
LU-500: A Logo Benchmark for Concept Unlearning Scalable logo recognition in real-world images
Reference 21
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Observation 01cfbaa5-bfc1-4834-958e-073884fad2cd · outbound
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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Observation a5d6e4ff-0559-486b-b0b5-f2fc782da8c9 · outbound
LU-500: A Logo Benchmark for Concept Unlearning Unified concept editing in diffusion models
Reference 38
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Observation 4eceac89-47e5-4325-b4f3-b6310421018b · outbound
LU-500: A Logo Benchmark for Concept Unlearning Mace: Mass concept erasure in diffusion models
Reference 39
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Observation 3f85f100-ca58-42cf-b362-7827d5f883a6 · outbound
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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Observation 7d0046dd-9816-4447-a4a7-8abffad44dc1 · outbound
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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Observation d99a8623-36a0-4eda-908d-071ea66352f5 · outbound
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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Observation 6ab8d8f2-46bd-4532-8658-79ee79c3fb94 · outbound
LU-500: A Logo Benchmark for Concept Unlearning Ablating concepts in text-to-image diffusion models
Reference 43
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Observation fc66c3bf-3e28-46b5-9d28-acac0cede4be · outbound
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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Observation 18818ffc-e20f-461b-8285-4417b26a8024 · outbound
LU-500: A Logo Benchmark for Concept Unlearning DatasetResearch: Benchmarking Agent Systems for Demand-Driven Dataset Discovery
Reference 45
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Observation e87e7e36-1b36-4d65-b35e-eed4dc64a240 · outbound
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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Observation ac88f77c-8038-491f-830c-459983d4213e · outbound
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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Observation fe8e3563-33ba-46fa-8bb1-75612095c16c · outbound
LU-500: A Logo Benchmark for Concept Unlearning Midjourney (V5.2) [Text-to-Image Model], 2023
Reference 48
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Observation f45c13af-b37e-4f55-95fa-083493a6e60b · outbound
LU-500: A Logo Benchmark for Concept Unlearning Dalle3, 2024
Reference 49
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Observation b5837851-3bc4-4fe3-aa8c-bb4a16e9d132 · outbound
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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Observation 0a9b3070-e49d-4bd8-b906-9c87a1a17801 · outbound
LU-500: A Logo Benchmark for Concept Unlearning Learning transferable visual models from natural language supervision
Reference 51
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Observation eae90705-2932-44ff-8383-ea43649a2e67 · outbound
LU-500: A Logo Benchmark for Concept Unlearning stable-diffusion-v1-5, 2022
Reference 52
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Observation 1be57c19-da89-4f75-8b13-7da0800415b3 · outbound
LU-500: A Logo Benchmark for Concept Unlearning A computational approach to edge detection, 1986
Reference 53
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Observation 9597b58b-6c7f-4ed4-840e-823ab18443fb · outbound
LU-500: A Logo Benchmark for Concept Unlearning Texture characterization based on grey-level co-occurrence matrix
Reference 54
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Observation 427a33b6-51c7-4eef-95b7-ba2617dc39fd · outbound
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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Observation 74fff4d5-8ffc-4778-92fa-2f75466247e7 · outbound
Reference 56
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Observation 0b651a6f-7cca-4343-8baa-8181f2f84f7f · outbound
LU-500: A Logo Benchmark for Concept Unlearning Unresolved cited work
Reference 57
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Observation e7ca2795-eff7-4267-a1ae-a52c9656428f · outbound
LU-500: A Logo Benchmark for Concept Unlearning Unresolved cited work
Reference 58
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Observation 684d3f14-f0c2-4428-aa07-6fe1c25f0aa1 · outbound
LU-500: A Logo Benchmark for Concept Unlearning Unresolved cited work
Reference 59
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Observation e134c442-3622-42e7-9d43-a4509ae61913 · outbound
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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Observation 1eece3f1-5954-4f8f-ab46-dd37b6dee4a4 · outbound
LU-500: A Logo Benchmark for Concept Unlearning Keep the prompts simple, without complex scenes
Reference 61
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Observation 7d478e43-dc88-41fa-8cb5-4574c68f622e · outbound
LU-500: A Logo Benchmark for Concept Unlearning Unresolved cited work
Reference 62
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Observation a52777a0-e7fe-4d14-bb78-7d5e3b28b656 · outbound
LU-500: A Logo Benchmark for Concept Unlearning Unresolved cited work
Reference 63
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Observation 9e5cc5af-7f56-442b-bc7f-4089a165c32f · outbound
LU-500: A Logo Benchmark for Concept Unlearning Unresolved cited work
Reference 64
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Observation a2023017-c606-4158-ba59-c325153befec · outbound
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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Observation a42039bd-3c56-4ace-9994-9d03ffc8c424 · outbound
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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No inbound Pith citation observations are available.