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Paper Citation Record · LEDGER

Finite Sample Analysis and Bounds of Generalization Error of Gradient Descent in In-Context Linear Regression

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.02462.

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

pith.paper-citation-record.v1
2405.02462 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:42:08.887996Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T05:16:20.961317Z

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 eb1251fe-4819-42d0-b142-b4df18a1c9d9 · inbound

Training Dynamics of In-Context Learning in Linear Attention cites this paper.

Training Dynamics of In-Context Learning in Linear Attention Finite Sample Analysis and Bounds of Generalization Error of Gradient Descent in In-Context Linear Regression

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-10T13:42:08.887996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T13:42:08.887996Z digest=sha256:261ee342c748b9ed1c37de046bbfeabfca36b8de6b1e9bef4f5f147efbc372a1

Observation ae0ca787-76c9-4307-ab80-230dc6ab1875 · inbound

Is In-Context Universality Enough? MLPs are Also Universal In-Context cites this paper.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Finite Sample Analysis and Bounds of Generalization Error of Gradient Descent in In-Context Linear Regression

Reference 22

Resolution
verified exact
local_arxiv, observed 2026-08-09T05:16:20.965615Z

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=2026-08-09T05:16:19.863290Z digest=sha256:de21b13d0c971984247ecdfa7c7c9fba484bb0da04e0070f8150b1a84f0b2804