Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-06-29T22:56:43.393302Z
Paper Citation Record · LEDGER
Unified Neural Scaling Laws
As of 4 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2605.26248.
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
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
30 of 30 outbound references displayed
- verified exact12
- verified fuzzy0
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch14
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 92720e21-d356-46a4-870c-bb0438a3add5 · outbound
This paper cites Revisiting neural scaling laws in language and vision.
Unified Neural Scaling Laws Revisiting neural scaling laws in language and vision
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T22:56:43.393302Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ded477fb-e46d-4881-ab10-1252003f3a14 · outbound
This paper cites Explaining Neural Scaling Laws.
Reference 2
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T23:44:02.780941Z
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 7f1b0def-28c0-4f73-bb57-b737af7f5a81 · outbound
This paper cites Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al.
Unified Neural Scaling Laws Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T22:56:43.393302Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e4469700-8701-48ee-9602-bd35c4b850b2 · outbound
This paper cites Broken Neural Scaling Laws.
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T23:44:02.768783Z
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 1e97d7bf-66f6-4c69-b567-d5e9d555c644 · outbound
This paper cites Leveraging procedural generation to benchmark reinforcement learning.
Unified Neural Scaling Laws Leveraging procedural generation to benchmark reinforcement learning
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T22:56:43.393302Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9020ff1e-9aa4-4b80-9ed9-ec61b328e730 · outbound
This paper cites Karin Dahmen and James P.
Unified Neural Scaling Laws Karin Dahmen and James P
Reference 6
- Resolution
- metadata mismatch
- doi, observed 2026-06-29T23:04:00.783898Z
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 30d1807a-4009-402c-b7f9-35eb2aca00c2 · outbound
This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
Unified Neural Scaling Laws An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T23:44:02.802271Z
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 961d062b-3897-41a6-9c47-7218239ea025 · outbound
This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.
Unified Neural Scaling Laws Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T23:44:03.376455Z
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 3506b622-c8cd-4c65-96e3-74f37500e5d6 · outbound
This paper cites Scaling Laws for Autoregressive Generative Modeling.
Unified Neural Scaling Laws Scaling Laws for Autoregressive Generative Modeling
Reference 9
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-29T23:44:02.790523Z
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 8d1a6a52-e7e7-41e4-9998-83daf80bc95c · outbound
This paper cites Scaling Laws for Transfer.
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T23:44:03.370701Z
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 2b27d33d-9f65-4844-8fcf-11af0c013ebe · outbound
This paper cites Deep Learning Scaling is Predictable, Empirically.
Unified Neural Scaling Laws Deep Learning Scaling is Predictable, Empirically
Reference 11
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-29T23:44:03.365102Z
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 719437c9-6b3a-4e62-b126-ab6bf2194b83 · outbound
This paper cites Scaling laws for single-agent reinforcement learning.
Unified Neural Scaling Laws Scaling laws for single-agent reinforcement learning
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T23:44:03.368224Z
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 9b28b091-ad3a-4904-a22d-ad9f1815654f · outbound
This paper cites Training Compute-Optimal Large Language Models.
Unified Neural Scaling Laws Training Compute-Optimal Large Language Models
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T23:44:03.356896Z
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 e3e46e52-6736-4dd7-8fcc-e7d3c4b11412 · outbound
This paper cites Approximation capabilities of multilayer feedforward networks.
Unified Neural Scaling Laws Approximation capabilities of multilayer feedforward networks
Reference 14
- Resolution
- metadata mismatch
- doi, observed 2026-06-29T23:04:00.777385Z
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 b21272ef-64df-4c33-a38b-aba0c7ebce38 · outbound
This paper cites Scaling Laws for Neural Language Models.
Unified Neural Scaling Laws Scaling Laws for Neural Language Models
Reference 15
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-29T23:44:03.359341Z
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 76144c07-6680-4004-9394-3ee5b506337c · outbound
This paper cites doi: https://doi.org/10.1016/S0893-6080(05)80131-5.
Unified Neural Scaling Laws doi: https://doi.org/10.1016/S0893-6080(05)80131-5
Reference 16
- Resolution
- metadata mismatch
- doi, observed 2026-06-29T23:04:00.781047Z
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 6d9c276a-fec6-4380-9b93-ddce28c31189 · outbound
This paper cites Scaling laws for diffusion transformers.
Unified Neural Scaling Laws Scaling laws for diffusion transformers
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T23:44:03.350471Z
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 1685a797-8d5c-4414-b686-15cddf1620ce · outbound
This paper cites An Empirical Model of Large-Batch Training.
Unified Neural Scaling Laws An Empirical Model of Large-Batch Training
Reference 18
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-29T23:44:03.353422Z
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 5f900871-077e-479c-aeb0-8242b59f06d7 · outbound
This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.
Unified Neural Scaling Laws Can a suit of armor conduct electricity? a new dataset for open book question answering
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T22:56:43.393302Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 76eef3de-6115-4309-bbfe-86524d69924e · outbound
This paper cites Scaling Data-Constrained Language Models.
Unified Neural Scaling Laws Scaling Data-Constrained Language Models
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T23:44:03.373589Z
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 5840e183-b397-4edb-8f43-19014c9fa0ef · outbound
This paper cites Deep Double Descent: Where Bigger Models and More Data Hurt.
Unified Neural Scaling Laws Deep Double Descent: Where Bigger Models and More Data Hurt
Reference 21
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T23:44:03.362215Z
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 4a5ac36a-c1b4-4282-a76d-88a346a594a7 · outbound
This paper cites Scaling Laws for a Multi-Agent Reinforcement Learning Model.
Unified Neural Scaling Laws Scaling Laws for a Multi-Agent Reinforcement Learning Model
Reference 22
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-30T00:14:04.514809Z
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 24dc3aec-bcc9-45f0-a29f-7f212d0efa55 · outbound
This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.
Unified Neural Scaling Laws Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets
Reference 23
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T23:44:02.758784Z
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 2a6d89ca-88a1-4caa-aa8a-1d9ff501c6db · outbound
This paper cites A Constructive Prediction of the Generalization Error Across Scales.
Unified Neural Scaling Laws A Constructive Prediction of the Generalization Error Across Scales
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T00:14:04.517837Z
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 20af7bd4-ff35-4eb5-8d5c-9b8d2dfd48a6 · outbound
This paper cites Scaling Laws for Linear Complexity Language Models.
Unified Neural Scaling Laws Scaling Laws for Linear Complexity Language Models
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T23:44:02.762246Z
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 0f4df87e-8c42-4df3-b728-3e9396d021c4 · outbound
This paper cites Don't Decay the Learning Rate, Increase the Batch Size.
Unified Neural Scaling Laws Don't Decay the Learning Rate, Increase the Batch Size
Reference 27
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-29T23:44:03.339083Z
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 ea870fa7-ecef-4878-9cb3-c56f69aa402e · outbound
This paper cites Freeze-Thaw Bayesian Optimization.
Unified Neural Scaling Laws Freeze-Thaw Bayesian Optimization
Reference 28
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-29T23:44:03.341940Z
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 8c8492ca-96bf-428a-b8bd-ec0762b8846e · outbound
This paper cites Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer.
Unified Neural Scaling Laws Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer
Reference 29
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T23:44:03.344613Z
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 cc81bd36-f1a5-4a6c-b1d1-240da1e3096b · outbound
This paper cites Large Batch Training of Convolutional Networks.
Unified Neural Scaling Laws Large Batch Training of Convolutional Networks
Reference 30
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T23:44:03.347192Z
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 96e3459a-788e-4879-b74a-317410fee9eb · outbound
This paper cites Scaling Vision Transformers.
Reference 32
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T23:44:03.336396Z
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
No inbound Pith citation observations are available.