Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-02T16:07:05.189001Z
Paper Citation Record · LEDGER
K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks
As of 9 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 0 inbound Pith citation observations for arXiv:2607.00329.
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 2026-08-09T06:31:02.800959+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
9 of 9 outbound references displayed
- verified exact3
- verified fuzzy5
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c69dbda7-e970-40cd-9340-202bb5d5c068 · outbound
This paper cites Mechanism for feature learning in neural networks and backpropagation-free machine learning models.
K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks Mechanism for feature learning in neural networks and backpropagation-free machine learning models
Reference 1
- Resolution
- verified exact
- doi, observed 2026-07-02T16:07:07.943092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3c4c764c-0f49-4f5f-88bf-1e9912991d6a · outbound
This paper cites Kernel methods.
K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks Kernel methods
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T23:21:36.226822Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3a454221-e4ca-416b-b5cb-fee7c6328e97 · outbound
This paper cites Wahba.Spline Models for Observational Data, volume 59 ofCBMS-NSF Regional Conference Series in Applied Mathematics.
K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks Wahba.Spline Models for Observational Data, volume 59 ofCBMS-NSF Regional Conference Series in Applied Mathematics
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T23:21:36.234120Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8b641fd5-8c62-4355-b149-a2c948b7657a · outbound
This paper cites Emergence in non-neural models: grokking modular arithmetic via average gradient outer product.
K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks Emergence in non-neural models: grokking modular arithmetic via average gradient outer product
Reference 4
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T16:07:08.195458Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 04d420b4-6eaa-4b4d-852c-80852e9f932d · outbound
This paper cites Toward universal steering and monitoring of AI models.
K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks Toward universal steering and monitoring of AI models
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T16:07:08.192626Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation d626de17-d773-43e0-b827-7638e0f39ece · outbound
This paper cites Linear recursive feature machines provably recover low-rank matrices.Proceedings of the National Academy of Sciences, 122 (13):e2411325122.
K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks Linear recursive feature machines provably recover low-rank matrices.Proceedings of the National Academy of Sciences, 122 (13):e2411325122
Reference 6
- Resolution
- verified exact
- doi, observed 2026-07-02T16:07:07.940755Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation fc9eb094-7ef2-46f1-abb1-b209d3586997 · outbound
This paper cites Average gradient outer product as a mechanism for deep neural collapse.
K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks Average gradient outer product as a mechanism for deep neural collapse
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T23:21:36.221075Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation d36fdbdb-a93c-4f4f-9548-12515e555f14 · outbound
This paper cites The k-inverse rfm framework.
K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks The k-inverse rfm framework
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T23:21:36.225140Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 9caff14a-8a48-4357-a426-415a675595bf · outbound
This paper cites Deep learning through the lens of example difficulty.
K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks Deep learning through the lens of example difficulty
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T23:21:36.231470Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Pith citing papers
No inbound Pith citation observations are available.