Paper Citation Record · LEDGER

Learning to Learn with Generative Models of Neural Network Checkpoints

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2209.12892.

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

pith.paper-citation-record.v1
2209.12892 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:30:29.622367Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T13:29:51.910822Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2ac43cb0-698d-4ebb-ad13-0e8315c7b5ee · inbound

Scalable Diffusion Models with Transformers cites this paper.

Scalable Diffusion Models with Transformers Learning to Learn with Generative Models of Neural Network Checkpoints

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:04:05.741960Z

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 c9d4cddd-ffbc-4dae-ac6d-f5fdedd5c3d6 · inbound

Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios cites this paper.

Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios Learning to Learn with Generative Models of Neural Network Checkpoints

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T21:30:29.622367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb8b8e2d-ec87-4c59-9aeb-44d007b62f13 · inbound

Text2Weight: Bridging Natural Language and Neural Network Weight Spaces cites this paper.

Text2Weight: Bridging Natural Language and Neural Network Weight Spaces Learning to Learn with Generative Models of Neural Network Checkpoints

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-05T19:04:27.334988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9a60cc0-c65c-4577-848c-e5d4fbda2cd3 · inbound

Conflicting Scores, Confusing Signals: An Empirical Study of Vulnerability Scoring Systems cites this paper.

Conflicting Scores, Confusing Signals: An Empirical Study of Vulnerability Scoring Systems Learning to Learn with Generative Models of Neural Network Checkpoints

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-05T18:59:49.925030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation beee7f66-3ea4-4d1b-8940-e9d434647bcb · inbound

Hyper Diffusion Avatars: Dynamic Human Avatar Generation using Network Weight Space Diffusion cites this paper.

Hyper Diffusion Avatars: Dynamic Human Avatar Generation using Network Weight Space Diffusion Learning to Learn with Generative Models of Neural Network Checkpoints

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-05T10:24:49.294637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c9e960f-6924-4a9a-8523-7b02f82cc3b3 · inbound

Semantic-guided LoRA Parameters Generation cites this paper.

Semantic-guided LoRA Parameters Generation Learning to Learn with Generative Models of Neural Network Checkpoints

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-05T05:37:06.534636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70bd3169-0e68-4fa7-a4c2-4d62ab7dd61b · inbound

Weight Space Representation Learning via Neural Field Adaptation cites this paper.

Weight Space Representation Learning via Neural Field Adaptation Learning to Learn with Generative Models of Neural Network Checkpoints

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T19:13:32.696419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca27d3bb-591e-47ae-bfde-499053712d75 · inbound

Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching cites this paper.

Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching Learning to Learn with Generative Models of Neural Network Checkpoints

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-13T17:19:02.982053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7aeea76-d71f-4d69-9102-cd4e03bb20da · inbound

Robotic Policy Adaptation via Weight-Space Meta-Learning cites this paper.

Robotic Policy Adaptation via Weight-Space Meta-Learning Learning to Learn with Generative Models of Neural Network Checkpoints

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-02T18:57:17.203617Z

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 2b431ba4-c28f-4cc5-bab0-897ff82eb736 · inbound

Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork cites this paper.

Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork Learning to Learn with Generative Models of Neural Network Checkpoints

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T13:29:51.912140Z

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.