Paper Citation Record · LEDGER

Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2502.21212.

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

pith.paper-citation-record.v1
2502.21212 v1

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 2026-08-12T06:34:41.77262+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:13:06.748375Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T14:33:30.564243Z

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 04a28cdf-1d77-4bec-8a79-869984e94306 · inbound

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective cites this paper.

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:06.748375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fb45c7f-1a84-423b-a32d-f6bf87f6f129 · 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 Learn to Implement Multi-step Gradient Descent with Chain of Thought

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:35.648813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c0353aa-28ea-4756-a534-246b4d84a5bd · inbound

Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning cites this paper.

Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:39.805726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20322a7a-6bb9-45f5-b225-73b09a4fd085 · inbound

Scaling Latent Reasoning via Looped Language Models cites this paper.

Scaling Latent Reasoning via Looped Language Models Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-15T07:43:11.868463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9f3afce4-bad0-40fd-816b-e86b429d8a5c · inbound

Scaling Latent Reasoning via Looped Language Models cites this paper.

Scaling Latent Reasoning via Looped Language Models Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:11.198585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4bc93b8-17f3-4004-af50-aa0d241b74ba · inbound

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

The Power of Power Law: Asymmetry Enables Compositional Reasoning Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:31:08.209336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1a30b7a3-8988-4d53-8a5f-c0c3e2cbcef5 · inbound

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

The Power of Power Law: Asymmetry Enables Compositional Reasoning Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought

Reference 23

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fca0c2da-bbf3-459b-86dd-bad78171dcfe · inbound

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

Transformers Provably Learn to Internalize Chain-of-Thought Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:33:30.565722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.