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Paper Citation Record · LEDGER

A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2408.12578.

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

pith.paper-citation-record.v1
2408.12578 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:05:29.293988Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:49:29.685632Z

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 a5efbd5b-4113-4105-932e-d7643408b95e · inbound

A ghost mechanism: An analytical model of abrupt learning in recurrent networks cites this paper.

A ghost mechanism: An analytical model of abrupt learning in recurrent networks A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-23T06:32:38.812252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-23T06:31:21.006691Z digest=sha256:b6f6bda697ebb4a2796190a44654c3175e7e5081adc2e494e4b7ac76c5278c59

Observation 3466d3f9-47d8-47f2-8dfb-6605a6ebaeea · inbound

Decomposing Elements of Problem Solving: What "Math" Does RL Teach? cites this paper.

Decomposing Elements of Problem Solving: What "Math" Does RL Teach? A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-07T13:05:29.293988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:05:29.293988Z digest=sha256:9ef2aa80b16ebb43ebe2d2969e75597f2237b94f1acccdfc539248e37a56d1a6

Observation e5c3f89e-5e6d-451f-867a-be182066aa9d · inbound

Multispin Physics of AI Tipping Points and Hallucinations cites this paper.

Multispin Physics of AI Tipping Points and Hallucinations A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T05:57:33.934759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:57:33.934759Z digest=sha256:2560267dc79f9db697ba199698319a11083d77fb79068b0986075cfa8954fb28

Observation 448e45bd-381a-40a6-8d17-848cd58d515f · inbound

Mechanisms of Misgeneralization in Physical Sequence Modeling cites this paper.

Mechanisms of Misgeneralization in Physical Sequence Modeling A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language

Reference 64

Resolution
metadata mismatch
arxiv_id, observed 2026-05-21T07:44:48.656617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-21T07:44:37.810511Z digest=sha256:0cf2f70edc8e5c99fc84d562ed7c796a62d49969c8cf1f89312188d7356453ff

Observation 01f6a44d-9941-443e-9296-7f27747ede24 · inbound

Critical Percolation as a Synthetic Data Model for Interpretability cites this paper.

Critical Percolation as a Synthetic Data Model for Interpretability A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language

Reference 34

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T03:49:29.687630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T17:41:29.317167Z digest=sha256:201437d8b2b0119861a3b3c52881c19599fc3b1632f4673f1d57ce6fd2d93e07