Paper Citation Record · LEDGER

Measuring the Algorithmic Efficiency of Neural Networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2005.04305.

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

pith.paper-citation-record.v1
2005.04305 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:07:39.517278Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

87
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 78845b65-68e0-4dbc-aae4-3e862e1e528c · inbound

Scaling Laws for Transfer cites this paper.

Scaling Laws for Transfer Measuring the Algorithmic Efficiency of Neural Networks

Reference 171

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:58:13.550799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d16a73e6-b498-4584-b72d-b5728417f7df · inbound

A General Language Assistant as a Laboratory for Alignment cites this paper.

A General Language Assistant as a Laboratory for Alignment Measuring the Algorithmic Efficiency of Neural Networks

Reference 55

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T14:22:59.305783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8ad2a7db-e6c6-4627-8a4c-990adda35773 · inbound

Scaling Laws and Interpretability of Learning from Repeated Data cites this paper.

Scaling Laws and Interpretability of Learning from Repeated Data Measuring the Algorithmic Efficiency of Neural Networks

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:52:40.541293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7e3c62f6-479b-4e24-a60b-62debbae5437 · inbound

Language Models (Mostly) Know What They Know cites this paper.

Language Models (Mostly) Know What They Know Measuring the Algorithmic Efficiency of Neural Networks

Reference 112

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T15:42:47.706384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 983dcfe7-b288-45fb-9d9d-5b300cfdfd01 · inbound

A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search cites this paper.

A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search Measuring the Algorithmic Efficiency of Neural Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:07:39.517278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63ea51e3-f1db-416a-99ed-f3a8a6a20a36 · inbound

ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution cites this paper.

ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution Measuring the Algorithmic Efficiency of Neural Networks

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T13:58:58.755032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c4004afd-4455-4b9c-9944-522500ced26b · inbound

Continued AI Scaling Requires Repeated Efficiency Doublings cites this paper.

Continued AI Scaling Requires Repeated Efficiency Doublings Measuring the Algorithmic Efficiency of Neural Networks

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:32:58.917556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 199f772b-056e-4e2a-95d0-b76ccc7ee6f0 · inbound

Scalable Reinforcement Learning via Adaptive Batch Scaling cites this paper.

Scalable Reinforcement Learning via Adaptive Batch Scaling Measuring the Algorithmic Efficiency of Neural Networks

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:24:27.770606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 188be391-c54d-4b5c-aab0-228a4fca561f · inbound

Scalable Reinforcement Learning via Adaptive Batch Scaling cites this paper.

Scalable Reinforcement Learning via Adaptive Batch Scaling Measuring the Algorithmic Efficiency of Neural Networks

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:24:57.661945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1ebbb067-60d5-4de5-87eb-d0c3971e04b4 · inbound

The Neuromorphic Supremacy cites this paper.

The Neuromorphic Supremacy Measuring the Algorithmic Efficiency of Neural Networks

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:26:24.539143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 192d1009-b387-4e87-b788-badf8ebf7f4b · inbound

Sakana Fugu Technical Report cites this paper.

Sakana Fugu Technical Report Measuring the Algorithmic Efficiency of Neural Networks

Reference 63

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T06:39:36.903759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 98a9dbf1-07c8-448b-823d-5a7d30f1235c · inbound

Hybrid Quantum Neural Networks: Theory, Implementations, and Applications cites this paper.

Hybrid Quantum Neural Networks: Theory, Implementations, and Applications Measuring the Algorithmic Efficiency of Neural Networks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T00:30:46.699421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.