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Paper Citation Record · LEDGER

Emergent properties with repeated examples

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.07041.

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

pith.paper-citation-record.v1
2410.07041 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:40:56.425685Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T14:29:54.905471Z

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 2e41c6ef-512c-41e0-aca8-aefd4b56ab63 · inbound

Uncovering a Universal Abstract Algorithm for Modular Addition in Neural Networks cites this paper.

Uncovering a Universal Abstract Algorithm for Modular Addition in Neural Networks Emergent properties with repeated examples

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T14:40:56.425685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:40:56.425685Z digest=sha256:aa57bf7da6a7f8ed3783858b9ed21d081752818d054a9aa7b0c4606f326dcfef

Observation 2ad212d8-b944-4178-8d4d-5d1b7c075383 · inbound

Transformers Pretrained on Procedural Data Contain Modular Structures for Algorithmic Reasoning cites this paper.

Transformers Pretrained on Procedural Data Contain Modular Structures for Algorithmic Reasoning Emergent properties with repeated examples

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-07T13:14:25.167243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:14:25.167243Z digest=sha256:85db87210278fd57faa9d74f030c9cdda55d4ff6e714d452ce4356739d92e693

Observation 277f067b-01c2-48a9-a67b-66d353505652 · inbound

Learning Pseudorandom Numbers with Transformers: Permuted Congruential Generators, Curricula, and Interpretability cites this paper.

Learning Pseudorandom Numbers with Transformers: Permuted Congruential Generators, Curricula, and Interpretability Emergent properties with repeated examples

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-04T07:21:33.898071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T07:21:33.898071Z digest=sha256:f6ef7b56248bfa61d79a6c0bf4432ea2c4c610c6fa96626ede14e9f62313e389

Observation e9283003-0789-4128-b460-2396b4a7cfb7 · inbound

Procedural Pretraining: Warming Up Language Models with Abstract Data cites this paper.

Procedural Pretraining: Warming Up Language Models with Abstract Data Emergent properties with repeated examples

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-03T06:55:10.044488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T06:55:10.044488Z digest=sha256:96faa5e579f3be964016ef13b380401a621a71ce89ae3467bf1b7be1d4aacfbb

Observation e9ceb735-cfa1-4278-9840-591e8a76d190 · inbound

The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently cites this paper.

The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently Emergent properties with repeated examples

Reference 134

Resolution
verified exact
arxiv_id, observed 2026-05-12T05:31:24.136856Z

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=2026-05-12T05:27:11.761971Z digest=sha256:42fd4e28b839f5138685b42acaa00ffc09db63665a30e453730d7ec8002a3e96

Observation 2348cc57-02a1-4a00-9e74-e616c39c3502 · inbound

Generating Special Triangulations with Transformers cites this paper.

Generating Special Triangulations with Transformers Emergent properties with repeated examples

Reference 58

Resolution
verified exact
arxiv_id, observed 2026-07-04T14:29:54.907510Z

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=2026-06-26T03:27:36.821748Z digest=sha256:71cc8c0c0688893731a420fecadcc22ebe012678f5343fe41a66485b1731a2bd