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Paper Citation Record · LEDGER

How Well Can Transformers Emulate In-context Newton's Method?

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2403.03183.

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

pith.paper-citation-record.v1
2403.03183 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:35:15.813064Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T08:23:15.186509Z

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 f1c9b27e-6001-4152-9f6f-a7a8989e7263 · inbound

Re-examining learning linear functions in context cites this paper.

Re-examining learning linear functions in context How Well Can Transformers Emulate In-context Newton's Method?

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-12T18:35:15.813064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:35:15.813064Z digest=sha256:fde14d9ec5cb90933ce2f4fcdaf151699c2b402612edd86d393c4abdf624c612

Observation 2fd659ec-5ad5-4404-882d-f8f2bc0922cd · inbound

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

Transformers versus the EM Algorithm in Multi-class Clustering How Well Can Transformers Emulate In-context Newton's Method?

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-08T17:13:25.085621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T17:13:25.085621Z digest=sha256:363c2e776e38573098fe13fc4d7b5831ec114cdf09e5e7196741fb15cf62cdcf

Observation d9332bc3-54f7-44cc-b35e-e8692ef160ff · inbound

Transformers Meet In-Context Learning: A Universal Approximation Theory cites this paper.

Transformers Meet In-Context Learning: A Universal Approximation Theory How Well Can Transformers Emulate In-context Newton's Method?

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T10:33:37.489293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:33:37.489293Z digest=sha256:d01609685206afab2bb35264ca539f2e16d211d904a60931c1e079dd4f5095bc

Observation 1182d2c6-3d90-46b6-a411-627e77ed7d65 · inbound

SSA: Improving Performance With a Better Scoring Function cites this paper.

SSA: Improving Performance With a Better Scoring Function How Well Can Transformers Emulate In-context Newton's Method?

Reference 7

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T22:01:52.062406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-18T21:58:28.070835Z digest=sha256:e91184213ee6e22b7eddaa3fe64336a9f530e665ba5cb674fa1a30a3aaa0c7b9

Observation 140ffa02-c7e7-4f4b-b6a2-0df7ee31375a · inbound

RL4RLA: Teaching ML to Discover Randomized Linear Algebra Algorithms Through Curriculum Design and Graph-Based Search cites this paper.

RL4RLA: Teaching ML to Discover Randomized Linear Algebra Algorithms Through Curriculum Design and Graph-Based Search How Well Can Transformers Emulate In-context Newton's Method?

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-20T12:53:17.594938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-20T12:52:03.539565Z digest=sha256:4e100290f28ab20f41d4d8aaaa221f819bdf532b27fff8b4e71e942f6d5de35e

Observation 1e6f8130-752d-44d8-9e7d-6e130a69f56d · inbound

In-Context Reward Adaptation for Robust Preference Modeling cites this paper.

In-Context Reward Adaptation for Robust Preference Modeling How Well Can Transformers Emulate In-context Newton's Method?

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-06-29T08:23:15.188089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-29T08:19:55.177440Z digest=sha256:fb953b070494b2b9044ff7febf0b730a045acfa55911d8b25608ba6ca2cdaa4f