Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 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 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:13:25.085621Z

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 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.

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.

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 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.