Paper Citation Record · LEDGER

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation

As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2505.19020.

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

pith.paper-citation-record.v1
2505.19020 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:24:33.508442Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

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

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:06:06.809058Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:06:08.680505Z

Reference resolution

43 of 43 outbound references displayed

verified exact1
verified fuzzy32
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f81d0c60-a899-4344-8010-5faeb73224e5 · outbound

This paper cites OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:24:33.724804Z

Source-reported events for the cited work

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

Observation bd42604e-5828-40fc-8f60-3ee9eb5c6950 · outbound

This paper cites LiGNN: Graph neural networks at LinkedIn.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation LiGNN: Graph neural networks at LinkedIn

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.505221Z

Source-reported events for the cited work

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

Observation 75d98d97-8234-44ec-bfc8-7e9d7a033fa1 · outbound

This paper cites LightGCL: Simple yet effective graph contrastive learning for recommendation.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation LightGCL: Simple yet effective graph contrastive learning for recommendation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.490863Z

Source-reported events for the cited work

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

Observation e6d36ae3-a2d6-4140-85a1-cefc13f31cb9 · outbound

This paper cites Macro graph neural networks for online billion-scale recommender systems.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Macro graph neural networks for online billion-scale recommender systems

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.476969Z

Source-reported events for the cited work

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

Observation f87874ee-354a-4eff-9635-33d8ab6126d4 · outbound

This paper cites Leveraging contrastive learning for enhanced node representations in tokenized graph transformers.Advances in Neural Information Processing Systems, 37:85824–85845, 2024.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Leveraging contrastive learning for enhanced node representations in tokenized graph transformers.Advances in Neural Information Processing Systems, 37:85824–85845, 2024

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.462194Z

Source-reported events for the cited work

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

Observation 235c1201-580e-4454-9e3f-942f97cb39bf · outbound

This paper cites Heteroge- neous graph contrastive learning for recommendation.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Heteroge- neous graph contrastive learning for recommendation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.447957Z

Source-reported events for the cited work

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

Observation 6ecb45c6-8e52-4b92-bbaf-6bf961f151ee · outbound

This paper cites A simple framework for contrastive learning of visual representations.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation A simple framework for contrastive learning of visual representations

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:30.775102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae6f9049-9058-4587-b753-d7f602b1d509 · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Understanding the difficulty of training deep feedforward neural networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:30.825253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a12cdba5-e9a7-4d46-ab2e-c6de14b24e5b · outbound

This paper cites Architecture matters: Uncovering implicit mechanisms in graph contrastive learning.Advances in Neural Information Processing Systems, 36:28585–28610, 2023.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Architecture matters: Uncovering implicit mechanisms in graph contrastive learning.Advances in Neural Information Processing Systems, 36:28585–28610, 2023

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.417675Z

Source-reported events for the cited work

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

Observation 25028439-d277-448a-a2db-83f52c5029e9 · outbound

This paper cites Exploitation of a latent mechanism in graph contrastive learning: Representation scattering.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Exploitation of a latent mechanism in graph contrastive learning: Representation scattering

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.404070Z

Source-reported events for the cited work

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

Observation 11f2f54d-5e27-4616-bdfa-0ec45ed3dc4c · outbound

This paper cites LightGCN: Simplifying and powering graph convolution network for recommendation.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation LightGCN: Simplifying and powering graph convolution network for recommendation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:31.082511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f64a4435-03c6-4583-960a-08e0f07b24a3 · outbound

This paper cites MixGCF: An improved training method for graph neural network-based recommender systems.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation MixGCF: An improved training method for graph neural network-based recommender systems

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.381689Z

Source-reported events for the cited work

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

Observation 04a4c10b-7596-4fb2-a873-413b5eda3c95 · outbound

This paper cites Towards Graph Contrastive Learning: A Survey and Beyond.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Towards Graph Contrastive Learning: A Survey and Beyond

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:31.185629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b27d895e-cbc2-4622-bf45-e90d42660267 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Adam: A Method for Stochastic Optimization

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:31.279183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ead92e5-6703-4f98-b98c-dbab20504cd7 · outbound

This paper cites Bootstrapping user and item representations for one-class collaborative filtering.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Bootstrapping user and item representations for one-class collaborative filtering

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.366120Z

Source-reported events for the cited work

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

Observation 4f24f28c-236c-40b6-aa8e-89f3952e4feb · outbound

This paper cites Hierarchical bipartite graph neural networks: Towards large-scale E-commerce applications.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Hierarchical bipartite graph neural networks: Towards large-scale E-commerce applications

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.199768Z

Source-reported events for the cited work

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

Observation 4870fbdb-af96-4d79-a101-69a5d8ada1d0 · outbound

This paper cites Variational autoen- coders for collaborative filtering.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Variational autoen- coders for collaborative filtering

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:36.896103Z

Source-reported events for the cited work

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

Observation f1aab98e-1ebd-48f7-908a-4c3e2335edd5 · outbound

This paper cites Improving graph collaborative filtering with neighborhood-enriched contrastive learning.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Improving graph collaborative filtering with neighborhood-enriched contrastive learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:36.784154Z

Source-reported events for the cited work

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

Observation 3b6c08b8-ae89-4df9-b239-c8e387e3e53c · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Representation Learning with Contrastive Predictive Coding

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:31.661493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66ece9fb-3cb0-47e8-991f-7ea4bf246e4c · outbound

This paper cites BPR: Bayesian Personalized Ranking from Implicit Feedback.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation BPR: Bayesian Personalized Ranking from Implicit Feedback

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:31.713931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2be8038d-9129-4f8d-a9ff-6a13e38fbeda · outbound

This paper cites Visualizing data using t-SNE.Journal of Machine Learning Research, 9(11), 2008.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Visualizing data using t-SNE.Journal of Machine Learning Research, 9(11), 2008

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:36.603289Z

Source-reported events for the cited work

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

Observation 65b2072a-cbf2-47f9-a3e3-5742765019e4 · outbound

This paper cites Deep Graph Infomax.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Deep Graph Infomax

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:31.889674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c9e336b-8663-4fe6-903f-d11f7c0dfe87 · outbound

This paper cites Neural graph collabo- rative filtering.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Neural graph collabo- rative filtering

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:36.468964Z

Source-reported events for the cited work

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

Observation ce828d0e-3a2a-4323-9279-7556b6e6a273 · outbound

This paper cites Self-supervised heterogeneous graph neural network with co-contrastive learning.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Self-supervised heterogeneous graph neural network with co-contrastive learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:36.254305Z

Source-reported events for the cited work

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

Observation 8a85305e-a8cf-43e6-a6c8-35a3cbdeba34 · outbound

This paper cites Self-supervised graph learning for recommendation.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Self-supervised graph learning for recommendation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:36.149821Z

Source-reported events for the cited work

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

Observation 48b9942a-6b45-4e88-9b09-3c0ae2c574e3 · outbound

This paper cites Representing long-range context for graph neural networks with global attention.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Representing long-range context for graph neural networks with global attention

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:35.894554Z

Source-reported events for the cited work

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

Observation 29a1f2cb-767e-4cad-bfc8-58de3cd9dcd5 · outbound

This paper cites Hyper- graph contrastive collaborative filtering.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Hyper- graph contrastive collaborative filtering

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:35.680992Z

Source-reported events for the cited work

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

Observation 08d74116-4861-4d83-8d04-d49b9699be74 · outbound

This paper cites Simple and asymmetric graph contrastive learning without augmentations.Advances in Neural Information Processing Systems, 36:16129–16152, 2023.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Simple and asymmetric graph contrastive learning without augmentations.Advances in Neural Information Processing Systems, 36:16129–16152, 2023

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:35.518844Z

Source-reported events for the cited work

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

Observation b7eaf8ff-e08d-48b8-afe8-a2ca2868a480 · outbound

This paper cites InfoGCL: Information-aware graph contrastive learning.Advances in Neural Information Processing Systems, 34:30414–30425, 2021.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation InfoGCL: Information-aware graph contrastive learning.Advances in Neural Information Processing Systems, 34:30414–30425, 2021

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:35.290025Z

Source-reported events for the cited work

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

Observation 8577ac8d-530b-41da-b733-e1a4a89d21a3 · outbound

This paper cites Self-supervised graph- level representation learning with local and global structure.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Self-supervised graph- level representation learning with local and global structure

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:35.175968Z

Source-reported events for the cited work

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

Observation 37402df5-0add-448f-9c5b-dcce81d714a1 · outbound

This paper cites Predicting individual irregular mobility via web search-driven bipartite graph neural networks.IEEE Transactions on Knowledge and Data Engineering, 2024.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Predicting individual irregular mobility via web search-driven bipartite graph neural networks.IEEE Transactions on Knowledge and Data Engineering, 2024

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:35.023326Z

Source-reported events for the cited work

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

Observation 4dc00a1f-949d-44b8-b546-ef78c5cbf318 · outbound

This paper cites Hierarchical graph contrastive learning.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Hierarchical graph contrastive learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.949424Z

Source-reported events for the cited work

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

Observation 333a1d7c-6350-402a-831d-c844e8e1f239 · outbound

This paper cites An empirical study towards prompt-tuning for graph contrastive pre-training in recommendations.Advances in Neural Information Processing Systems, 36:62853–62868, 2023.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation An empirical study towards prompt-tuning for graph contrastive pre-training in recommendations.Advances in Neural Information Processing Systems, 36:62853–62868, 2023

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.813405Z

Source-reported events for the cited work

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

Observation 32f1bc23-e215-4382-96e0-e9ca68c8e9df · outbound

This paper cites Self-supervised learning for large-scale item recommendations.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Self-supervised learning for large-scale item recommendations

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.680802Z

Source-reported events for the cited work

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

Observation dd68e346-168c-4ef1-8934-30ac3e393cca · outbound

This paper cites Graph convolutional neural networks for web-scale recommender systems.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Graph convolutional neural networks for web-scale recommender systems

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.609620Z

Source-reported events for the cited work

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

Observation 2ca59752-3612-48e8-82b1-7b2270390f38 · outbound

This paper cites Hierarchical graph representation learning with differentiable pooling.Advances in Neural Information Processing Systems, 31, 2018.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Hierarchical graph representation learning with differentiable pooling.Advances in Neural Information Processing Systems, 31, 2018

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:33.015892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d93999f-3c36-47e1-81a7-87cbd36c5e45 · outbound

This paper cites Graph contrastive learning with augmentations.Advances in Neural Information Processing Systems, 33:5812–5823, 2020.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Graph contrastive learning with augmentations.Advances in Neural Information Processing Systems, 33:5812–5823, 2020

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.459864Z

Source-reported events for the cited work

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

Observation 540a010c-6508-455c-9483-f151c3f9929d · outbound

This paper cites XSimGCL: Towards extremely simple graph contrastive learning for recommendation.IEEE Transactions on Knowledge and Data Engineering, 36(2):913–926, 2023.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation XSimGCL: Towards extremely simple graph contrastive learning for recommendation.IEEE Transactions on Knowledge and Data Engineering, 36(2):913–926, 2023

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.346861Z

Source-reported events for the cited work

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

Observation 05c2c8cf-2f57-4614-b2c5-c6c5bb5fff14 · outbound

This paper cites Are graph augmentations necessary? Simple graph contrastive learning for recommendation.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Are graph augmentations necessary? Simple graph contrastive learning for recommendation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.210994Z

Source-reported events for the cited work

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

Observation 7f4eeffe-8781-4e41-bee1-bb8e0d56585e · outbound

This paper cites Self-supervised learning for recommender systems: A survey.IEEE Transactions on Knowledge and Data Engineering, 36(1):335–355, 2023.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Self-supervised learning for recommender systems: A survey.IEEE Transactions on Knowledge and Data Engineering, 36(1):335–355, 2023

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.088700Z

Source-reported events for the cited work

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

Observation 009ac8de-738b-42bc-80f8-0bf9563a3ab8 · outbound

This paper cites ContextGNN: Beyond two-tower recommendation systems.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation ContextGNN: Beyond two-tower recommendation systems

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:33.998395Z

Source-reported events for the cited work

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

Observation 1daf01da-8091-4b75-b137-a4a8f6165021 · outbound

This paper cites Long-tail augmented graph con- trastive learning for recommendation.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Long-tail augmented graph con- trastive learning for recommendation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:33.840753Z

Source-reported events for the cited work

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

Observation a569c23b-4002-4662-9299-e79467170a79 · outbound

This paper cites Deep Graph Contrastive Representation Learning.

HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation Deep Graph Contrastive Representation Learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:33.508442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 9ccbd0f8-9c3f-416f-845c-ccfb116c5f09 · inbound

GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models cites this paper.

GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:06:08.746586Z

Source-reported events for the cited work

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