Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-03T17:03:25.354819Z
Paper Citation Record · LEDGER
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
As of 15 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2607.02166.
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 2026-08-15T06:32:42.880941+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
24 of 24 outbound references displayed
- verified exact11
- verified fuzzy9
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch2
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 96583d3d-92c6-4892-848d-673b608ceb83 · outbound
This paper cites Spatial Functa: Scaling Functa to ImageNet Classification and Generation.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Spatial Functa: Scaling Functa to ImageNet Classification and Generation
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T17:08:42.729152Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 4d1330c8-bfea-478a-acc9-98cd981e0d82 · outbound
This paper cites Deep Learning on Implicit Neural Representations of Shapes.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Deep Learning on Implicit Neural Representations of Shapes
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T17:08:42.745387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 066bd899-5c81-46cf-ae9c-05b6527f8388 · outbound
This paper cites From data to functa: Your data point is a function and you can treat it like one.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space From data to functa: Your data point is a function and you can treat it like one
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T17:08:42.734125Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation f660bafa-bd7b-4359-a356-c7b707d069d3 · outbound
This paper cites Local Deep Implicit Functions for 3D Shape.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Local Deep Implicit Functions for 3D Shape
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T17:08:42.739514Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ae9f80fa-3cff-4598-99c1-9026ad6dda79 · outbound
This paper cites Implicit Geometric Regularization for Learning Shapes.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Implicit Geometric Regularization for Learning Shapes
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T17:08:42.742620Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 0b11bb70-d36d-40ae-98eb-41c9085730a4 · outbound
This paper cites W2t: Lora weights already know what they can do.arXiv preprint arXiv:2603.15990, 2026a.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space W2t: Lora weights already know what they can do.arXiv preprint arXiv:2603.15990, 2026a
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T17:08:42.737009Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 1c0a3222-4d2d-4bec-9ec5-6d83d5d04791 · outbound
This paper cites Representation learning for dynamic graphs: A survey.Journal of Machine Learning Research, 21(70):1–73,.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Representation learning for dynamic graphs: A survey.Journal of Machine Learning Research, 21(70):1–73,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:00:42.189285Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d6094792-5576-410c-bbf7-64927b7129e0 · outbound
This paper cites Graph Neural Networks for Learning Equivariant Representations of Neural Networks.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T17:08:42.742350Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 945edc38-9b24-431d-beb2-b9081a16eb09 · outbound
This paper cites VeLO: Training Versatile Learned Optimizers by Scaling Up.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space VeLO: Training Versatile Learned Optimizers by Scaling Up
Reference 9
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T17:08:42.740013Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b5db805a-3598-482c-b520-aa8a1c167a93 · outbound
This paper cites Temporal Graph Networks for Deep Learning on Dynamic Graphs.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Temporal Graph Networks for Deep Learning on Dynamic Graphs
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T17:08:42.731073Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2559219f-0b0a-439d-9c9d-b4707a653d22 · outbound
This paper cites Structured sequence model- ing with graph convolutional recurrent networks.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Structured sequence model- ing with graph convolutional recurrent networks
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:00:42.187313Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a3ae3bd1-bdf1-4e10-931c-bdb64bc576ec · outbound
This paper cites Improved Generalization of Weight Space Networks via Augmentations.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Improved Generalization of Weight Space Networks via Augmentations
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T17:08:42.731971Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 68c14cbb-eea6-4681-846e-39578cd51e9c · outbound
This paper cites Directed Acyclic Graph Neural Networks.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Directed Acyclic Graph Neural Networks
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T17:08:42.737409Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a60b66a9-63f4-4b1c-9e2c-2d413cbf885c · outbound
This paper cites Predicting Neural Network Accuracy from Weights.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Predicting Neural Network Accuracy from Weights
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T17:08:42.728472Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 445b7307-4c5b-4804-b347-6fb79c3f0463 · outbound
This paper cites Graph HyperNetworks for Neural Architecture Search.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Graph HyperNetworks for Neural Architecture Search
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T17:08:42.721164Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 19abf8a0-fde8-4fb5-8907-d6e7b310a61b · outbound
This paper cites Permutation equivariant neural functionals.Advances in Neural Information Processing Systems, 36, 2024a.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Permutation equivariant neural functionals.Advances in Neural Information Processing Systems, 36, 2024a
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:00:42.180951Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 6a7e0650-9ea6-43b3-9e38-d7cddb385d05 · outbound
This paper cites inverse problem.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space inverse problem
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:00:42.183194Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a176939e-8540-4f6e-9ec6-60752a443bf8 · outbound
This paper cites an unresolved cited work.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Unresolved cited work
Reference 18
- Resolution
- unresolved
- raw_fallback, observed 2026-07-05T05:00:42.178525Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 757ed599-557c-491f-a3a0-5c7a0688027d · outbound
This paper cites Under this convention, πl acts on the nodesvl and edgesel as follows: •Nodes:The permuted nodes satisfy ˜vl =P πl vl.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Under this convention, πl acts on the nodesvl and edgesel as follows: •Nodes:The permuted nodes satisfy ˜vl =P πl vl
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:00:42.185295Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation cf3a78a9-3ac2-4ff5-b733-7af4d5f85536 · outbound
This paper cites By induction,GT is equivariant under neuron permutations at each layer.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space By induction,GT is equivariant under neuron permutations at each layer
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:00:42.178711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 81d553a0-579b-4a3c-9159-c767cc68de37 · outbound
This paper cites Under permutationπ: •Added nodesv l becomev l′={π(i)|vl i∈vl}.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Under permutationπ: •Added nodesv l becomev l′={π(i)|vl i∈vl}
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:00:42.176468Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 75ca72a4-e1da-47bd-a53c-90dc38a81c5a · outbound
This paper cites Specifically, a residual connection in a neural network allows the input to bypass one or more layers and be added directly to the output.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Specifically, a residual connection in a neural network allows the input to bypass one or more layers and be added directly to the output
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:00:42.176652Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ffbe92ea-79e1-453e-b817-cb3186bbb7a9 · outbound
This paper cites an unresolved cited work.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space Unresolved cited work
Reference 23
- Resolution
- unresolved
- raw_fallback, observed 2026-07-05T05:00:42.166618Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 02751353-c02f-4d81-90d9-13a325c53eaa · outbound
This paper cites We set the training batch size to 128, use Adam as the optimizer with learning rate of 1e-4, train for 200 epochs, and use early stopping.
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space We set the training batch size to 128, use Adam as the optimizer with learning rate of 1e-4, train for 200 epochs, and use early stopping
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:00:42.171727Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Pith citing papers
No inbound Pith citation observations are available.