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Paper Citation Record · LEDGER

From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2410.05459.

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

pith.paper-citation-record.v1
2410.05459 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 2026-08-05T06:32:48.257954+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T05:27:44.529687Z

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 42f0197e-30c9-46bb-a399-aea76b88e17d · inbound

Breaking the Reversal Curse in Autoregressive Language Models via Identity Bridge cites this paper.

Breaking the Reversal Curse in Autoregressive Language Models via Identity Bridge From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-03T05:27:44.529687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:27:44.529687Z digest=sha256:c83215aa64362adf38a3ee1bede0664c137c6e3689619a0e1be66ba42e2b0bf0

Observation 4f8fbc10-55d4-40ce-82d7-6c4d511cdeff · inbound

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

On the Emergence of Implicit Curriculum in RLVR Learning Dynamics From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-02T23:11:56.681936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T23:11:56.681936Z digest=sha256:235ad1eee2ea4a3d37538bbdaa545fbfd0358b909f69401a2fe1c325f0027a65

Observation 34b2affc-a800-43ae-86ad-2d78e95b882a · inbound

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

The Power of Power Law: Asymmetry Enables Compositional Reasoning From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:31:08.381738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-05-08T11:49:49.787123Z digest=sha256:166d3a2692f890dddfad6b46d2d4e1fa6ecd7dd6a3c338b15b61210515a4c2a5

Observation 8a1e30ca-2190-41fe-84d1-9b468517f84d · inbound

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

The Power of Power Law: Asymmetry Enables Compositional Reasoning From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency

Reference 54

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=sha256:7cfaac187f556d5bb2d5a41a21d656d82521b7bf23c1110d7191c6f8798b05a3

Observation fe5c8a4d-2b15-42e1-a141-fe0270cbd952 · inbound

The two clocks and the innovation window: When and how generative models learn rules cites this paper.

The two clocks and the innovation window: When and how generative models learn rules From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T03:16:18.110627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-05-12T03:15:45.257213Z digest=sha256:1a1f9845e841f73b6de89556ac04d39575e10d0dc2a29fb025be69046912a289

Observation 4d011ce6-1534-47bc-9fb4-589d17a05c06 · inbound

Steered Generation via Gradient-Based Optimization on Sparse Query Features cites this paper.

Steered Generation via Gradient-Based Optimization on Sparse Query Features From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:36:40.299551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-25T05:31:29.510639Z digest=sha256:5665e2ee06d51e16b196b4960866ffe5b8fb88f742c07805a2ea74cfeeb4d1fe

Observation eb03379a-a679-44aa-9a4b-782d9255ef4b · inbound

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

Transformers Provably Learn to Internalize Chain-of-Thought From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-06-29T14:33:30.594670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-29T14:29:10.010212Z digest=sha256:2479b10684f50b0725a38fcdcbcf3a14d4844fc08afbe28f64229ad869ca1adf

Observation 9d47d62b-7dbe-4f2f-90bf-83035e95c9f1 · inbound

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

Agentic Transformers Provably Learn to Search via Reinforcement Learning From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency

Reference 30

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T23:42:49.906343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T23:26:28.158991Z digest=sha256:b219bc09b2e4e3d3bd70676d47e437f7cccfdae40bee07deaf16025fe5c6ee96