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Paper Citation Record · LEDGER

Transformers Provably Solve Parity Efficiently with Chain of Thought

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2410.08633.

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

pith.paper-citation-record.v1
2410.08633 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:37:40.682061Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5faf387d-a41d-4915-a41a-42faf968e9ea · inbound

Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation cites this paper.

Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-09T17:37:40.682061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T17:37:40.682061Z digest=sha256:dfadaa3b055578cfbfb278f5274382f641cbf9ee50252775ced274de1379989f

Observation e50a8057-42ac-4f97-ad9d-e92a09eb5f9e · inbound

What makes a good feedforward computational graph? cites this paper.

What makes a good feedforward computational graph? Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-08T14:33:15.961026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T14:33:15.961026Z digest=sha256:eadd220f6f8e2874bfa86de3087919f949657722b582aedb0690529dbbe54cfb

Observation 2322fda7-1f28-4a84-bb51-4dec2ba0ffc8 · inbound

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

Learning Compositional Functions with Transformers from Easy-to-Hard Data Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T12:46:36.416475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:46:36.416475Z digest=sha256:076deb9ce2f64c763da9f69f43cb6580bb509d4110590e689f79aab9fef397d1

Observation 09bcebd1-1f60-478e-90b7-79b246d1c87f · inbound

RACE-Align: Retrieval-Augmented and Chain-of-Thought Enhanced Preference Alignment for Large Language Models cites this paper.

RACE-Align: Retrieval-Augmented and Chain-of-Thought Enhanced Preference Alignment for Large Language Models Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T11:21:41.931755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:41.931755Z digest=sha256:bbfb0713d9f37373c0a577fa8f5d67e60e5ac6071e84ef5c9e4c0a03dc0812f9

Observation 761845ca-59b7-41cb-b021-e26d2f542296 · inbound

Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression cites this paper.

Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-05T22:10:17.500880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:10:17.500880Z digest=sha256:369a2b4972eaa593eed64705f5f76e053e92af5b79e7438a356531feae41e54f

Observation ef7b5b22-72aa-4beb-a331-eb51b3ce4e25 · inbound

On the Emergence of Implicit Curriculum in RLVR Learning Dynamics cites this paper.

On the Emergence of Implicit Curriculum in RLVR Learning Dynamics Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-02T23:11:54.462324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T23:11:54.462324Z digest=sha256:562e222358497cac305d04f6fd5716292093fdfd54adfb522e8b15b767fab303

Observation dc5a69b5-2553-4fb8-bd71-9f739f2bdc06 · inbound

The Power of Power Law: Asymmetry Enables Compositional Reasoning cites this paper.

The Power of Power Law: Asymmetry Enables Compositional Reasoning Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:31:08.235090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-08T11:49:49.787123Z digest=sha256:ac134ab6715964f2e220ac5c285d64ca48d3a65aac1f4ad717b886cde89a551d

Observation 309d9737-4ca8-40b9-936c-65dd0430c10a · inbound

The Power of Power Law: Asymmetry Enables Compositional Reasoning cites this paper.

The Power of Power Law: Asymmetry Enables Compositional Reasoning Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 30

Resolution
unresolved
no resolver link, observed 2026-07-12T18:26:05.728364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T18:26:05.728364Z digest=sha256:8f0c1cbaabb49386a6339a51ad18a585ae37929164d004025da0077d8f7e8dd2

Observation 7e7bb8b7-8911-4419-a4cf-5553b441b1e2 · inbound

Learning to Think from Multiple Thinkers cites this paper.

Learning to Think from Multiple Thinkers Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-11T21:56:10.679647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T03:55:02.562122Z digest=sha256:b678d13199292bfa5727c240da4cdbf87dfda8c46e45e443ffb8f9d39a0733f0

Observation 46afb2d4-fd10-401b-8945-4c748efa40c2 · inbound

A Theory of Online Learning with Autoregressive Chain-of-Thought Reasoning cites this paper.

A Theory of Online Learning with Autoregressive Chain-of-Thought Reasoning Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:05:58.591059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-11T00:52:10.419984Z digest=sha256:f09f02daf98b19189ccd714180d5111689d3596db5a8954d5675fc95e19ec5e6

Observation b21e1c4a-dd87-4bde-bdd1-3a38f86547f5 · inbound

Transformers Provably Learn to Internalize Chain-of-Thought cites this paper.

Transformers Provably Learn to Internalize Chain-of-Thought Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-06-29T14:33:30.608253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-29T14:29:10.010212Z digest=sha256:f206390584ff602651030d877431bb14096fedc0c170def61490f90463d31ef5

Observation 669df209-6638-45a9-8514-ef59b29132a0 · inbound

Agentic Transformers Provably Learn to Search via Reinforcement Learning cites this paper.

Agentic Transformers Provably Learn to Search via Reinforcement Learning Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 31

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T23:42:49.908741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-28T23:26:28.158991Z digest=sha256:4d00661f80fd87ce9ffc069357ba8c60688d0c39a998b6646f6aa2d7992006f8

Observation f58d5fc5-fedc-4321-a2b9-bf18c2a9f70b · inbound

Learning through Internalization cites this paper.

Learning through Internalization Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-06-26T17:49:40.652802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-26T17:43:18.915404Z digest=sha256:5ceaf254ee21496553f57e3384b80eeae0e02991480ec5d4227a7976fa1f0591