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Paper Citation Record · LEDGER

Levels of Analysis for Machine Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2004.05107.

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

pith.paper-citation-record.v1
2004.05107 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:11:41.829744Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:40:57.731221Z

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 d9fee2d3-ad25-40c8-97f5-292ace72c0e8 · inbound

The Geometry of Tokens in Internal Representations of Large Language Models cites this paper.

The Geometry of Tokens in Internal Representations of Large Language Models Levels of Analysis for Machine Learning

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-10T19:11:41.829744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:11:41.829744Z digest=sha256:3efcace9f02e65013aaf951c6795a590bb1055c4f58d073fb34c62c52be3e91f

Observation d9915aaa-6aa8-4b09-b845-c1be79042c9a · 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 Levels of Analysis for Machine Learning

Reference 19

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:40:57.796916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T14:40:55.233422Z digest=sha256:a55eafaeb64043d13658d73d93fdf93c137c773918de470d7ed59109ead96d27