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Paper Citation Record · LEDGER

Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2408.14511.

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

pith.paper-citation-record.v1
2408.14511 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:37:40.653083Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T23:42:49.971036Z

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 5a07c5ea-c178-4f41-9fb7-c849e8acde89 · 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 Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-09T17:37:40.653083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T17:37:40.653083Z digest=sha256:09451f2fccf958ad90daba7dc3574b47c23087955e020ab3e32c27aebb83c600

Observation 991ff95d-50e5-4e9e-9e51-95dfff229baf · inbound

CoT Information: Improved Sample Complexity under Chain-of-Thought Supervision cites this paper.

CoT Information: Improved Sample Complexity under Chain-of-Thought Supervision Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods

Reference 2305

Resolution
unresolved
no resolver link, observed 2026-08-07T15:22:13.111877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:22:13.111877Z digest=sha256:52e804863fdbfcb72d2efacdffbee1c3df66cfbe32aa727ce616007293140660

Observation e39cf4a8-d83e-42c7-ae6d-5d18a02680d0 · 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 Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-05T22:10:17.474987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:10:17.474987Z digest=sha256:053296508b780ee7671f10eab03ec56252f156b4d3ac995e74b222f3d37a68b9

Observation d8c87431-3a4a-4265-a19f-cbd086685744 · inbound

Optimal Self-Consistency for Efficient Reasoning with Large Language Models cites this paper.

Optimal Self-Consistency for Efficient Reasoning with Large Language Models Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-03T22:09:27.590448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:09:27.590448Z digest=sha256:d083bcc2cce8a422ce5b5e1f0dfceca1263423d243e203b5dde383e82023245a

Observation 07b6708d-f5a0-471d-a5d6-9402c6712df4 · inbound

Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently cites this paper.

Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-03T20:57:10.684434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T20:57:10.684434Z digest=sha256:c4bf20c29975d3f65f1f4bd239e21e79562b26532f214f6222cf97f999bf8306

Observation 410f35f2-f8a6-49ec-96b6-d874071044b8 · inbound

On the Cost and Benefit of Chain of Thought: A Learning-Theoretic Perspective cites this paper.

On the Cost and Benefit of Chain of Thought: A Learning-Theoretic Perspective Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-21T05:13:58.670659Z

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=2026-05-21T05:09:37.588841Z digest=sha256:a47a5779d289768aefe286a56d8ff388eea24b6330909bbb51e0515c5b3d556e

Observation b50d5328-d296-42fc-8b19-c1f56eebd572 · inbound

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

Agentic Transformers Provably Learn to Search via Reinforcement Learning Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-06-28T23:42:49.972232Z

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=arxiv_source observed=2026-06-28T23:26:28.158991Z digest=sha256:193c7b423c4826d248239a1c1f1795ae22c3f049dc7ff107551be4864232261b

Observation d5d415da-b896-4aa1-a52d-ff78dba5072d · inbound

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion cites this paper.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-02T02:24:36.178641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:24:36.178641Z digest=sha256:234e656c6acaa3386d411ebf80a32f4e046952e5301b6946084384ff8f4181d5