Paper Citation Record · LEDGER

On the Computational Power of Transformers and its Implications in Sequence Modeling

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2006.09286.

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

pith.paper-citation-record.v1
2006.09286 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:27:33.593229Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T20:51:50.763304Z

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 9be132c5-0d99-4745-b608-25468cad540b · inbound

Learning Elementary Cellular Automata with Transformers cites this paper.

Learning Elementary Cellular Automata with Transformers On the Computational Power of Transformers and its Implications in Sequence Modeling

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T04:27:33.593229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4750f14a-ce43-44d6-863a-bc30b1a5f2d7 · inbound

Learning Spectral Methods by Transformers cites this paper.

Learning Spectral Methods by Transformers On the Computational Power of Transformers and its Implications in Sequence Modeling

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T22:36:51.186169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8573e64e-48b6-4e42-9380-0cc163251735 · inbound

Transformers versus the EM Algorithm in Multi-class Clustering cites this paper.

Transformers versus the EM Algorithm in Multi-class Clustering On the Computational Power of Transformers and its Implications in Sequence Modeling

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T17:13:25.003270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3da02f76-adfb-40e5-a345-516b08524857 · inbound

A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization cites this paper.

A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization On the Computational Power of Transformers and its Implications in Sequence Modeling

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T06:10:16.691808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7b1667b-9d5d-4fe2-941b-6cd2ac4243da · inbound

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques cites this paper.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques On the Computational Power of Transformers and its Implications in Sequence Modeling

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:34.215539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56899cd4-4c4a-4ef3-b630-cee31339b162 · inbound

Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling cites this paper.

Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling On the Computational Power of Transformers and its Implications in Sequence Modeling

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:51:50.765643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f35b7189-1d71-4af6-abbf-65e3f7486f1e · inbound

Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy cites this paper.

Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy On the Computational Power of Transformers and its Implications in Sequence Modeling

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-13T19:53:11.725699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.