Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-10T01:44:44.346732Z
Paper Citation Record · LEDGER
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction
As of 4 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2604.19550.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-31T14:57:40.679020Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
32 of 32 outbound references displayed
- verified exact27
- verified fuzzy2
- unresolved1
- parse uncertain0
- malformed identifier1
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 9a5c048b-fe94-4df8-a290-b80d4abe73a1 · outbound
This paper cites GPT-4 Technical Report.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction GPT-4 Technical Report
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T13:26:04.363373Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation bbd00b1c-1105-4f75-95b9-511b83a97c6c · outbound
This paper cites URL https://doi.org/10.1145/ 2959100.2959190.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction URL https://doi.org/10.1145/ 2959100.2959190
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T01:46:00.044533Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d8b4a1d5-0719-4d99-88bb-6164735651b0 · outbound
This paper cites Onepiece: Bringing context engineering and reasoning to industrial cascade ranking system.arXiv preprint arXiv:2509.18091.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Onepiece: Bringing context engineering and reasoning to industrial cascade ranking system.arXiv preprint arXiv:2509.18091
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:26:04.131632Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 78250541-1c19-4e39-8804-9f21fcde31e7 · outbound
This paper cites Universal Transformers.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Universal Transformers
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T08:04:31.534239Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 43c64a2f-26fa-4b03-b483-e405ee0251e1 · outbound
This paper cites Looped Transformers for Length Generalization.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Looped Transformers for Length Generalization
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:26:04.209043Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d15b17d2-fa0b-48fe-a9d5-1fb4cb64a383 · outbound
This paper cites Adaptive Computation Time for Recurrent Neural Networks.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Adaptive Computation Time for Recurrent Neural Networks
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T11:53:22.734695Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2c930dc5-6381-47fc-a95e-0e74ca6aec39 · outbound
This paper cites Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:26:04.224776Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 20f75c0f-f3f8-4c7a-880a-ef1d13a00d8e · outbound
This paper cites Training Compute-Optimal Large Language Models.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Training Compute-Optimal Large Language Models
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T13:26:04.157334Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 32c23a21-3794-4fe3-86aa-17be92763f27 · outbound
This paper cites MixFormer: Co-Scaling Up Dense and Sequence in Industrial Recommenders.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction MixFormer: Co-Scaling Up Dense and Sequence in Industrial Recommenders
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T02:16:49.181066Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0e99b918-560b-4786-b852-eaa3c5a3b305 · outbound
This paper cites Scaling Laws for Neural Language Models.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Scaling Laws for Neural Language Models
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T13:26:04.176212Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8fb4c3d8-76b9-4847-ba9c-1c8be401de8c · outbound
This paper cites Scaling recommender transformers to one billion parameters.arXiv preprint arXiv:2507.15994.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Scaling recommender transformers to one billion parameters.arXiv preprint arXiv:2507.15994
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:26:04.338509Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ef0c1c05-227a-4a0a-bda1-5ecd873c8c9e · outbound
This paper cites Encode, think, decode: Scaling test-time reasoning with recursive latent thoughts.CoRR, abs/2510.07358.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Encode, think, decode: Scaling test-time reasoning with recursive latent thoughts.CoRR, abs/2510.07358
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:26:04.280239Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 887388f1-be97-463b-a029-3875856c7960 · outbound
This paper cites ISBN 9798400702419.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction ISBN 9798400702419
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T01:46:00.041501Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6fd7ca89-4c6b-48b8-ab27-849a3fd7a663 · outbound
This paper cites doi: 10.1145/3343031.3350950.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction doi: 10.1145/3343031.3350950
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T01:46:00.046997Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b7f20b49-6beb-45f4-9644-ff8bf277fda2 · outbound
This paper cites Train flat, then compress: Sharpness-aware minimization learns more compressible models.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Train flat, then compress: Sharpness-aware minimization learns more compressible models
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T02:15:19.029356Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f5080f27-0995-4a7b-8240-6bb878ad6ad9 · outbound
This paper cites doi: 10.18653/v1/2022.findings-emnlp.361.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction doi: 10.18653/v1/2022.findings-emnlp.361
Reference 16
- Resolution
- verified exact
- doi, observed 2026-05-10T01:46:00.039018Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 43535ffe-f747-47b0-87eb-6c429a4ef336 · outbound
This paper cites Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T01:29:57.879683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ee6eec01-4d3f-4f85-8a24-bde7f53fc30e · outbound
This paper cites Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:26:04.218193Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ab8516a7-6fbb-465b-bd8e-260e9e8dbb89 · outbound
This paper cites Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T02:15:19.032162Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b244fb65-c3e2-465d-bbd1-915179d4bf64 · outbound
This paper cites mHC: Manifold-Constrained Hyper-Connections.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction mHC: Manifold-Constrained Hyper-Connections
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T12:31:48.157540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d73befe2-5ad7-4efd-a8ff-b150fb71da23 · outbound
This paper cites On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:26:04.270623Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 43a430d2-799b-412f-8470-f2994f9d9d96 · outbound
This paper cites Hhft: Hierarchical heterogeneous feature transformer for recommendation systems.arXiv preprint arXiv:2511.20235.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Hhft: Hierarchical heterogeneous feature transformer for recommendation systems.arXiv preprint arXiv:2511.20235
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:26:04.309172Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5f6ce521-b07a-47cb-9909-d185b7c68f03 · outbound
This paper cites doi: 10.1145/3746252.3761527.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction doi: 10.1145/3746252.3761527
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T01:46:00.034266Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ddcb6b7b-4725-4835-86c5-5f41088b0259 · outbound
This paper cites DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:26:04.190276Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 58b15122-9cb4-4fab-85c9-73622caba7dc · outbound
This paper cites Wukong: Towards a Scaling Law for Large-Scale Recommendation.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Wukong: Towards a Scaling Law for Large-Scale Recommendation
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:26:04.241119Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6f2c0eb7-e310-4a2c-8b19-387c2095b320 · outbound
This paper cites Zenith: Scaling up ranking models for billion-scale livestreaming recommendation.arXiv preprint arXiv:2601.21285.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Zenith: Scaling up ranking models for billion-scale livestreaming recommendation.arXiv preprint arXiv:2601.21285
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:26:04.251704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 283d663a-c7cd-46de-98ee-1c6d3066ec38 · outbound
This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp
Reference 27
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T01:46:00.037433Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 115975ef-9742-467c-be35-f49c73239257 · outbound
This paper cites Hyper-Connections.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Hyper-Connections
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:26:04.138897Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9157dc64-f6b7-4bdd-968b-7ad879f737eb · outbound
This paper cites Scaling Latent Reasoning via Looped Language Models.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Scaling Latent Reasoning via Looped Language Models
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T07:43:12.191103Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5199e271-4777-4c77-a514-26f54135f08f · outbound
This paper cites an unresolved cited work.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Unresolved cited work
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:26:04.286918Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c4f4367a-450b-4da7-8993-213510f9ef63 · outbound
This paper cites Let Tseq and Tglb denote the number of sequential and global tokens after long-term sequence compression, respectively, and let T= Tseq +T glb.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Let Tseq and Tglb denote the number of sequential and global tokens after long-term sequence compression, respectively, and let T= Tseq +T glb
Reference 31
- Resolution
- malformed identifier
- raw_fallback, observed 2026-05-23T02:15:19.043497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9e0f28db-a93d-421b-8897-7578850c01b3 · outbound
This paper cites an unresolved cited work.
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction Unresolved cited work
Reference 32
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T02:15:19.034719Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Pith citing papers
Observation a861e6b2-90ca-42d8-bcf0-4546f15aa15d · inbound
Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence cites this paper.
Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T14:57:40.679020Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.