Pith. sign in

Paper Citation Record · LEDGER

Transformers on Markov Data: Constant Depth Suffices

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2407.17686.

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

pith.paper-citation-record.v1
2407.17686 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 2026-08-22T06:32:14.747728+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:26:36.902854Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T00:07:17.208973Z

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 0086d19a-6307-4637-bf27-29e83a48177f · inbound

KV Shifting Attention Enhances Language Modeling cites this paper.

KV Shifting Attention Enhances Language Modeling Transformers on Markov Data: Constant Depth Suffices

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-12T10:10:29.907707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T10:10:29.907707Z digest=sha256:9eadf7516132f67466021a42b2d0ce4c37395504e59955b888bb0d1f0d93467d

Observation 8459a9ad-ffca-430c-876d-fac0d265d795 · inbound

The LZ78 Source cites this paper.

The LZ78 Source Transformers on Markov Data: Constant Depth Suffices

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-23T00:07:17.211893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-23T00:06:50.969268Z digest=sha256:64ce8aee961fc313dd66b96cb1ffc0225572bce3b2465438bc6900cc6da429f4

Observation 9fecf08d-16d4-41e8-9cb5-89e804bf80de · inbound

Pre-trained Large Language Models Learn Hidden Markov Models In-context cites this paper.

Pre-trained Large Language Models Learn Hidden Markov Models In-context Transformers on Markov Data: Constant Depth Suffices

Reference 42

Resolution
verified exact
arxiv_id, observed 2026-05-19T10:37:15.094419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-19T10:34:40.987583Z digest=sha256:43906e2670cdb4cc68ee1673d97529a74e9461e173593bf2b466e2d962ec731a

Observation 729f2995-ba6e-4df5-b876-53784724f649 · inbound

Learning In-context n-grams with Transformers: Sub-n-grams Are Near-stationary Points cites this paper.

Learning In-context n-grams with Transformers: Sub-n-grams Are Near-stationary Points Transformers on Markov Data: Constant Depth Suffices

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-15T17:26:36.902854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:26:36.902854Z digest=sha256:e912597c6848a51fb3d83bc1f3288c2c2748075d080e6198ccc34948f2b145c3

Observation a48610f4-91f2-43b5-9bf5-8b0a5f0f1bb3 · inbound

Selective Induction Heads: How Transformers Select Causal Structures In Context cites this paper.

Selective Induction Heads: How Transformers Select Causal Structures In Context Transformers on Markov Data: Constant Depth Suffices

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-04T21:11:44.263912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T21:11:44.263912Z digest=sha256:eeb24e639a0fae4237bcfcab9ef0d58093c396678417840274e1953e5b58e6b3

Observation fe242d53-4e3a-4b84-98e4-bc8ac54c0e9a · inbound

Transformers Learn Latent Mixture Models In-Context via Mirror Descent cites this paper.

Transformers Learn Latent Mixture Models In-Context via Mirror Descent Transformers on Markov Data: Constant Depth Suffices

Reference 1

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T10:41:05.038608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-10T15:22:21.955640Z digest=sha256:7c3bf2aa229e245be924c566d180ae817cf35fc4c344b745311bca9d965e3fa8