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Paper Citation Record · LEDGER

How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2009.11848.

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

pith.paper-citation-record.v1
2009.11848 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:35:38.694920Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T20:12:38.299479Z

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 40282564-b28f-4394-99a1-3545fadd11cc · inbound

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges cites this paper.

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

Reference 102

Resolution
verified exact
arxiv_id, observed 2026-05-13T02:39:30.041004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-13T02:39:29.411021Z digest=sha256:518326e08cf4054fed002d3851f35ea5abf93b1a66f2e6f35d0f1e2f99266d07

Observation 3c1b399f-6259-4e54-ab8a-c104186c7f9a · inbound

Massive Activations in Large Language Models cites this paper.

Massive Activations in Large Language Models How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

Reference 95

Resolution
verified exact
arxiv_id, observed 2026-05-16T07:02:53.944970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-16T07:02:53.740597Z digest=sha256:fe1dc23a7bec8482c2ac4c77dcf15e4f5beb6c249e8a1ac8b0127bfb3bb4bb09

Observation 814e2d97-23ef-4d59-8955-d1a7c985e1a3 · inbound

SLIDE: A machine-learning based method for forced dynamic response estimation of multibody systems cites this paper.

SLIDE: A machine-learning based method for forced dynamic response estimation of multibody systems How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-23T20:43:25.649278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-23T20:40:00.594670Z digest=sha256:3eafacc3b049537e599ec068d98344549b4e01f5d80d74c904a059ae8bf32272

Observation 12a86789-62b3-4553-8fad-adf99922c6b3 · inbound

How Far is Video Generation from World Model: A Physical Law Perspective cites this paper.

How Far is Video Generation from World Model: A Physical Law Perspective How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

Reference 13

Resolution
metadata mismatch
arxiv_id, observed 2026-05-20T11:13:41.467785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-20T11:13:41.407945Z digest=sha256:d0cc2e241d3a2c8162c3bed131a534e9ed746c642a549a157f8186b6f3df8ec1

Observation eace7a95-a835-45f7-be50-a9ddc999b708 · inbound

Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation cites this paper.

Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

Reference 95

Resolution
unresolved
no resolver link, observed 2026-08-10T20:35:38.694920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T20:35:38.694920Z digest=sha256:0003bc8999cc4498f63f0638d2d729dbe8aa0b8314af579f07306a9a66da0ba0

Observation 1d3a9796-8466-47d2-a191-923e4144fe8f · inbound

Evolution and The Knightian Blindspot of Machine Learning cites this paper.

Evolution and The Knightian Blindspot of Machine Learning How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

Reference 209

Resolution
unresolved
no resolver link, observed 2026-08-10T16:30:10.336593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:30:10.336593Z digest=sha256:8f141aa903aa6cb1bf12b423bc7fbc440bf8c372ff6d91bd3173330475f9e38c

Observation a2e8d594-2d08-4242-b589-9fd0748c403e · inbound

Beyond Interpolation: Extrapolative Reasoning with Reinforcement Learning and Graph Neural Networks cites this paper.

Beyond Interpolation: Extrapolative Reasoning with Reinforcement Learning and Graph Neural Networks How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-09T00:33:49.298172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T00:33:49.298172Z digest=sha256:663d2e6b2f530532ebc6106c96cea62e2548a36a486466af6e0546d835d663f5

Observation a1fc4e69-2abd-4db8-8131-c0d5979ecdea · inbound

Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators cites this paper.

Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-17T23:40:31.170691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-17T23:39:09.228699Z digest=sha256:c0daafef947daba77d829f86affa116690fe67a401a42d814c2c460674dd2799

Observation 4f6f6ac3-983f-4c1d-8a5c-a1b9699766c4 · inbound

Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach cites this paper.

Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

Reference 68

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T20:12:38.300780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-28T19:54:35.046899Z digest=sha256:4856f53b0a2245cda7c88da1c6369fadadacc839ac280a26e8ea9bc6d57a7116