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Paper Citation Record · LEDGER

Separations in the Representational Capabilities of Transformers and Recurrent Architectures

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.09347.

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

pith.paper-citation-record.v1
2406.09347 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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:36:27.113480Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:46:42.732602Z

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 06a43d3d-120e-4623-b936-d5482f1ad17c · inbound

Lower bounds on transformers with infinite precision cites this paper.

Lower bounds on transformers with infinite precision Separations in the Representational Capabilities of Transformers and Recurrent Architectures

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-10T23:36:27.113480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:36:27.113480Z digest=sha256:ea3495fc7b3892ae1c0e5816c2ab7ef6e7b8ec278f6291c07e3533c0c38793d0

Observation 36d3242c-1566-4cde-b737-5d22627eb4f5 · inbound

Learning Compositional Functions with Transformers from Easy-to-Hard Data cites this paper.

Learning Compositional Functions with Transformers from Easy-to-Hard Data Separations in the Representational Capabilities of Transformers and Recurrent Architectures

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:46:42.789421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T12:46:33.485218Z digest=sha256:153557c08e8ddb4bcec6d9dc3784884b22a0e532ace673341215a6d730c5fc43