Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T15:53:14.114061Z
Paper Citation Record · LEDGER
Dealing with Synthetic Data Contamination in Online Continual Learning
As of 16 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2411.13852.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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
Reference resolution
63 of 63 outbound references displayed
- verified exact2
- verified fuzzy45
- unresolved15
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 1380df0e-8539-4002-9965-3fd837048354 · outbound
This paper cites Memory aware synapses: Learning what (not) to forget.
Dealing with Synthetic Data Contamination in Online Continual Learning Memory aware synapses: Learning what (not) to forget
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.651863Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 924a1329-9d14-4ed7-b2a7-4473d0b4cd8e · outbound
This paper cites Online continual learning with maximal interfered retrieval.
Dealing with Synthetic Data Contamination in Online Continual Learning Online continual learning with maximal interfered retrieval
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.643337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 04484083-88d0-4de2-96a2-8e0d29ea75b4 · outbound
This paper cites Expert gate: Lifelong learning with a network of experts.
Dealing with Synthetic Data Contamination in Online Continual Learning Expert gate: Lifelong learning with a network of experts
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.634597Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6bb5e29c-44ac-431e-9c57-6ed2430a9557 · outbound
This paper cites Gradient based sample selection for online continual learning.
Dealing with Synthetic Data Contamination in Online Continual Learning Gradient based sample selection for online continual learning
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.626030Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5f02ff2a-d845-4146-97b7-a97467c24f38 · outbound
This paper cites Dark experience for general continual learning: a strong, simple baseline.
Dealing with Synthetic Data Contamination in Online Continual Learning Dark experience for general continual learning: a strong, simple baseline
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.617106Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fb338e4a-f7f1-4934-bda2-cf9ce7551e1a · outbound
This paper cites New Insights on Reducing Abrupt Representation Change in Online Continual Learning.
Dealing with Synthetic Data Contamination in Online Continual Learning New Insights on Reducing Abrupt Representation Change in Online Continual Learning
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:13.939135Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bd6ad1d3-1008-4b32-aeda-4d17b428b90e · outbound
This paper cites Riemannian walk for incremental learning: Understanding forgetting and intransigence.
Dealing with Synthetic Data Contamination in Online Continual Learning Riemannian walk for incremental learning: Understanding forgetting and intransigence
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.607933Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 451249d1-3f33-4b69-930d-a69cf5b2f738 · outbound
This paper cites Would Deep Generative Models Amplify Bias in Future Models?.
Dealing with Synthetic Data Contamination in Online Continual Learning Would Deep Generative Models Amplify Bias in Future Models?
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T15:53:14.192527Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f6ca6f38-8c00-42a7-8dbd-0453d62122a0 · outbound
This paper cites A simple framework for contrastive learning of visual representations.
Dealing with Synthetic Data Contamination in Online Continual Learning A simple framework for contrastive learning of visual representations
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.599251Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0eadefd7-c64e-48ff-9f3d-3ee26965b253 · outbound
This paper cites Lifelong machine learning.
Dealing with Synthetic Data Contamination in Online Continual Learning Lifelong machine learning
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.590644Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e12b2751-81c5-41a7-947d-879b1484ebb5 · outbound
This paper cites IN100pytorch: Pytorch implementation: Training resnets on imagenet-100.
Dealing with Synthetic Data Contamination in Online Continual Learning IN100pytorch: Pytorch implementation: Training resnets on imagenet-100
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.582038Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b83e4789-f338-40f1-b9b1-e940ab9e8f58 · outbound
This paper cites A continual learning survey: Defying forgetting in classification tasks.
Dealing with Synthetic Data Contamination in Online Continual Learning A continual learning survey: Defying forgetting in classification tasks
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.572949Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 725954a4-b26f-4038-87a2-ffa863e1ad9d · outbound
This paper cites Imagenet: A large-scale hierarchical image database.
Dealing with Synthetic Data Contamination in Online Continual Learning Imagenet: A large-scale hierarchical image database
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:13.960477Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0a02c63d-863b-4bb8-a679-346629bbca84 · outbound
This paper cites PathNet: Evolution Channels Gradient Descent in Super Neural Networks.
Dealing with Synthetic Data Contamination in Online Continual Learning PathNet: Evolution Channels Gradient Descent in Super Neural Networks
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:13.967520Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ed4ee023-c44b-40bc-9eeb-eedf6537ad4e · outbound
This paper cites Vector quantized diffusion model for text-to-image synthesis.
Dealing with Synthetic Data Contamination in Online Continual Learning Vector quantized diffusion model for text-to-image synthesis
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:13.970577Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9aebdfa5-7440-4512-8458-b696ae9d20d9 · outbound
This paper cites Online continual learning through mutual information maximization.
Dealing with Synthetic Data Contamination in Online Continual Learning Online continual learning through mutual information maximization
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.554333Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d3c869b3-507a-438b-93de-4c14184b873a · outbound
This paper cites Dealing with cross-task class discrimination in online continual learning.
Dealing with Synthetic Data Contamination in Online Continual Learning Dealing with cross-task class discrimination in online continual learning
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.544922Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 73cc1bc3-2afa-46a4-878e-2ee19aa69d0a · outbound
This paper cites Will large-scale generative models corrupt future datasets? In ICCV, pages 20555–20565, 2023.
Dealing with Synthetic Data Contamination in Online Continual Learning Will large-scale generative models corrupt future datasets? In ICCV, pages 20555–20565, 2023
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.536212Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 429154cd-a09c-4c3e-ac3a-789116ea9fff · outbound
This paper cites Denoising diffusion probabilistic models.
Dealing with Synthetic Data Contamination in Online Continual Learning Denoising diffusion probabilistic models
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:13.981648Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4034e01b-5d9f-4cc3-8b66-473965734f7f · outbound
This paper cites Learning a unified classifier incrementally via rebalancing.
Dealing with Synthetic Data Contamination in Online Continual Learning Learning a unified classifier incrementally via rebalancing
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.521687Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 46a730fd-1238-467e-9e39-3568b4bb3ae1 · outbound
This paper cites Selective experience replay for lifelong learning.
Dealing with Synthetic Data Contamination in Online Continual Learning Selective experience replay for lifelong learning
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.511146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7892310a-5094-4149-b041-d5c1196da2de · outbound
This paper cites Supervised contrastive learning.
Dealing with Synthetic Data Contamination in Online Continual Learning Supervised contrastive learning
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.502259Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 41ae5bb3-9adc-4acd-bf39-365e1bf866f6 · outbound
This paper cites Overcoming catastrophic forgetting in neural networks.
Dealing with Synthetic Data Contamination in Online Continual Learning Overcoming catastrophic forgetting in neural networks
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.493135Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9b1b28a6-07c1-442b-9e0e-1a6177ade78f · outbound
This paper cites Learning multiple layers of features from tiny images.
Dealing with Synthetic Data Contamination in Online Continual Learning Learning multiple layers of features from tiny images
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:13.996469Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 21866773-904e-4bc7-ab9d-50ed667eabbc · outbound
This paper cites Tiny imagenet visual recognition challenge.
Dealing with Synthetic Data Contamination in Online Continual Learning Tiny imagenet visual recognition challenge
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.478937Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c86328aa-5708-419e-b1d9-911cf91416c7 · outbound
This paper cites Overcoming catastrophic forgetting by incremental moment matching.
Dealing with Synthetic Data Contamination in Online Continual Learning Overcoming catastrophic forgetting by incremental moment matching
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.469597Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 10da2226-b725-40e1-acdc-b2b9f60046a5 · outbound
This paper cites Forgery-aware adaptive transformer for generalizable synthetic image detection.
Dealing with Synthetic Data Contamination in Online Continual Learning Forgery-aware adaptive transformer for generalizable synthetic image detection
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.460087Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 04b0522e-b3d8-4afd-9423-42ceaf46d26c · outbound
This paper cites Towards understanding the interplay of generative artificial intelligence and the internet.
Dealing with Synthetic Data Contamination in Online Continual Learning Towards understanding the interplay of generative artificial intelligence and the internet
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.451086Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 75c98934-1209-4d0f-83fd-98396f218d34 · outbound
This paper cites GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models.
Dealing with Synthetic Data Contamination in Online Continual Learning GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.441746Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5db37e3f-9486-41df-bf91-c7545288d931 · outbound
This paper cites Towards universal fake image detectors that generalize across generative models.
Dealing with Synthetic Data Contamination in Online Continual Learning Towards universal fake image detectors that generalize across generative models
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.432868Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ba80271b-952d-45e7-89ff-d38b93a4b542 · outbound
This paper cites Continual lifelong learning with neural networks: A review.
Dealing with Synthetic Data Contamination in Online Continual Learning Continual lifelong learning with neural networks: A review
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:14.015463Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6a15feb5-fa64-486e-8f99-67997c94b38b · outbound
This paper cites Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference.
Dealing with Synthetic Data Contamination in Online Continual Learning Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:14.018346Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c2e0b37f-9023-4924-a0fc-71d1c02e8111 · outbound
This paper cites Experi- ence replay for continual learning.
Dealing with Synthetic Data Contamination in Online Continual Learning Experi- ence replay for continual learning
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.419672Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e7fd3b9c-bd71-462b-b501-219341999eaa · outbound
This paper cites High- resolution image synthesis with latent diffusion models.
Dealing with Synthetic Data Contamination in Online Continual Learning High- resolution image synthesis with latent diffusion models
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:14.024681Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4a597488-d462-445a-bec6-1ac3dec6a550 · outbound
This paper cites Incremental learning through deep adaptation.
Dealing with Synthetic Data Contamination in Online Continual Learning Incremental learning through deep adaptation
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.406266Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3e1d364b-b9e5-420c-9208-b845f8c5e988 · outbound
This paper cites Progressive Neural Networks.
Dealing with Synthetic Data Contamination in Online Continual Learning Progressive Neural Networks
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:14.029915Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 91b554ac-eeed-4117-9fae-02194580132a · outbound
This paper cites Overcoming catastrophic forgetting with hard attention to the task.
Dealing with Synthetic Data Contamination in Online Continual Learning Overcoming catastrophic forgetting with hard attention to the task
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.397708Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5c2b4c0c-eb37-4ce2-9160-d0b309f7679e · outbound
This paper cites Three scenarios for continual learning.
Dealing with Synthetic Data Contamination in Online Continual Learning Three scenarios for continual learning
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:14.036327Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fe9bfb53-d66a-4f5f-ac53-9f8d2d38a2ed · outbound
This paper cites Visualizing data using t-sne.
Dealing with Synthetic Data Contamination in Online Continual Learning Visualizing data using t-sne
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:14.039099Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b5766b7a-4c84-426a-a4dd-81e0ab4e846b · outbound
This paper cites Random sampling with a reservoir.
Dealing with Synthetic Data Contamination in Online Continual Learning Random sampling with a reservoir
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:14.042056Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 12a9f7a2-dde5-474c-a5b5-cc499e143fc0 · outbound
This paper cites A Comprehensive Survey of Continual Learning: Theory, Method and Application.
Dealing with Synthetic Data Contamination in Online Continual Learning A Comprehensive Survey of Continual Learning: Theory, Method and Application
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:14.045368Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2ad12577-983a-4f41-8bde-829afe040818 · outbound
This paper cites Improving Plasticity in Online Continual Learning via Collaborative Learning.
Dealing with Synthetic Data Contamination in Online Continual Learning Improving Plasticity in Online Continual Learning via Collaborative Learning
Reference 42
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T15:53:14.141479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c57afa46-4bc2-4f23-92ec-61df3ce3093b · outbound
This paper cites Dualprompt: Complementary prompting for rehearsal-free continual learning.
Dealing with Synthetic Data Contamination in Online Continual Learning Dualprompt: Complementary prompting for rehearsal-free continual learning
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.379490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 396c93a5-8425-43d0-aee4-d5f526673123 · outbound
This paper cites Learning to prompt for continual learning.
Dealing with Synthetic Data Contamination in Online Continual Learning Learning to prompt for continual learning
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.369915Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 18aebb9c-4f6b-45ba-b32a-d98b5b124f48 · outbound
This paper cites Online prototype learning for online continual learning.
Dealing with Synthetic Data Contamination in Online Continual Learning Online prototype learning for online continual learning
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.361742Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8addbe06-174f-4b25-a4da-a7427e58fb3c · outbound
This paper cites an image of a <class_name>.
Dealing with Synthetic Data Contamination in Online Continual Learning an image of a <class_name>
Reference 46
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-12T15:53:14.352851Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4eb675db-c9b7-4e73-a49f-064c3deafce0 · outbound
This paper cites The partial augmentation is a weaker version of augmentation, consisting of random cropping with p = 0.5, followed by random horizontal flip with p = 0.5.
Dealing with Synthetic Data Contamination in Online Continual Learning The partial augmentation is a weaker version of augmentation, consisting of random cropping with p = 0.5, followed by random horizontal flip with p = 0.5
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.344446Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 87217b79-bc6e-4e36-8637-8aa755e1b666 · outbound
This paper cites The full augmentation strategy is a stronger version of augmentation.
Dealing with Synthetic Data Contamination in Online Continual Learning The full augmentation strategy is a stronger version of augmentation
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.336239Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b55c0cb4-17ee-42db-aba3-92214a6fccfb · outbound
This paper cites 22 Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.
Dealing with Synthetic Data Contamination in Online Continual Learning 22 Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.327924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5e926ec3-de04-481c-b0fa-c5220a3acdf3 · outbound
This paper cites Limitations.
Dealing with Synthetic Data Contamination in Online Continual Learning Limitations
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.319299Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 72198b58-9857-416c-aa57-e2e452419810 · outbound
This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.
Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not include theoretical results
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.310998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 906aaf06-9b51-48f1-a5b5-3734048b7df3 · outbound
This paper cites Guidelines: • The answer NA means that the paper does not include experiments.
Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not include experiments
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.302492Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9a9b88c0-12f6-4778-98ee-30e60db15f77 · outbound
This paper cites Guidelines: • The answer NA means that paper does not include experiments requiring code.
Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that paper does not include experiments requiring code
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.293919Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 555d8382-f06e-492f-bcad-fd1c2d7d811f · outbound
This paper cites Guidelines: • The answer NA means that the paper does not include experiments.
Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not include experiments
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.285028Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8917f7b2-cdaf-4df5-ab01-e7a6e74ab9c7 · outbound
This paper cites Guidelines: • The answer NA means that the paper does not include experiments.
Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not include experiments
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.276736Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 07dcf9a2-7dc0-46da-8386-712155aae2ee · outbound
This paper cites Guidelines: • The answer NA means that the paper does not include experiments.
Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not include experiments
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.268296Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 22f0920d-0710-41e7-a540-3a8d48aee92c · outbound
This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.258883Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e70caf06-1af1-43a6-a642-e3146864b30b · outbound
This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.
Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that there is no societal impact of the work performed
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.249582Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 05421a5c-ad2f-428d-9b5d-7656ba04f89a · outbound
This paper cites Guidelines: • The answer NA means that the paper poses no such risks.
Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper poses no such risks
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:53:14.102970Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7119ca27-fb71-4de7-903d-71293486f784 · outbound
This paper cites The licenses of existing assets are properly respected.
Dealing with Synthetic Data Contamination in Online Continual Learning The licenses of existing assets are properly respected
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.235801Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 95dbac65-a9fa-4b22-bd4d-b0e708024ce1 · outbound
This paper cites Guidelines: • The answer NA means that the paper does not release new assets.
Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not release new assets
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.227175Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 49937291-ed55-4c1a-9016-fd48cb1dc0d5 · outbound
This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.218346Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a9b95656-f4a9-4384-9aef-5e5ab39e5112 · outbound
This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:53:14.209268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.