Paper Citation Record · LEDGER

Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2501.19105.

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

pith.paper-citation-record.v1
2501.19105 v2

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

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:40:55.435466Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T06:40:24.874593Z

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 037fabfc-e9d2-45b8-95ff-5c80e8c68bb4 · inbound

On the Mechanisms of Weak-to-Strong Generalization: A Theoretical Perspective cites this paper.

On the Mechanisms of Weak-to-Strong Generalization: A Theoretical Perspective Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:55.435466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bac987d3-6335-4144-a009-a1c9e45343e9 · inbound

On Weak-to-Strong Generalization and f-Divergence cites this paper.

On Weak-to-Strong Generalization and f-Divergence Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T11:17:46.343068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d059723b-e854-40ca-bf68-f4170a488cfb · inbound

On the Blessing of Pre-training in Weak-to-Strong Generalization cites this paper.

On the Blessing of Pre-training in Weak-to-Strong Generalization Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss

Reference 121

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:36:08.618146Z

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 d32037b6-2823-4d5e-8002-042c3c35b1a4 · inbound

Weak-to-Strong Generalization is Nearly Inevitable (in Linear Models) cites this paper.

Weak-to-Strong Generalization is Nearly Inevitable (in Linear Models) Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:41:08.926889Z

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 a93ab811-3d46-45b7-bd10-201f37ea947d · inbound

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent cites this paper.

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss

Reference 252

Resolution: verified exact; arxiv_id, observed 2026-05-20T01:32:56.173948Z

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 6cce98df-ed5a-4539-976e-6918c90803c5 · inbound

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent cites this paper.

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss

Reference 252

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:40:24.877796Z

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.