Pith. sign in

Paper Citation Record · LEDGER

Retraining with Predicted Hard Labels Provably Increases Model Accuracy

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.11206.

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

pith.paper-citation-record.v1
2406.11206 v3

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 2026-08-16T06:30:59.297886+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:11:14.648794Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T06:40:24.853926Z

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 8bbafa47-6fa0-4958-8e87-dfad73cd2737 · inbound

Self-Improvement in Language Models: The Sharpening Mechanism cites this paper.

Self-Improvement in Language Models: The Sharpening Mechanism Retraining with Predicted Hard Labels Provably Increases Model Accuracy

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-12T00:11:14.648794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:11:14.648794Z digest=sha256:fd4d974b783cfa77d9df4b9fed8859ec58004d797614a7c1fb1be7659f7898f4

Observation 646644d7-ad3d-4695-86f7-cc3766a5d9db · inbound

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing cites this paper.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Retraining with Predicted Hard Labels Provably Increases Model Accuracy

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T15:32:12.139557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:32:12.139557Z digest=sha256:cdfb4f0bdc2e8eec1674282bd32ed10bd0c4db1061df4853e26e947d0e406587

Observation 898fd987-cb19-47e6-92fb-a8728b8a78ad · inbound

Power Distribution Bridges Sampling, Self-Reward RL, and Self-Distillation cites this paper.

Power Distribution Bridges Sampling, Self-Reward RL, and Self-Distillation Retraining with Predicted Hard Labels Provably Increases Model Accuracy

Reference 111

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:56:08.376239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-08T16:53:00.860162Z digest=sha256:5b682ac6125bb7afa1ff5bd3e493c6d04d470c9b1728a65189ddb2acb6338f98

Observation fcc0fbc6-6611-456b-a647-5a00b89b1622 · 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 Retraining with Predicted Hard Labels Provably Increases Model Accuracy

Reference 259

Resolution
verified exact
arxiv_id, observed 2026-05-20T01:32:55.952103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-20T01:29:14.555216Z digest=sha256:bf26ac2dc6a5795411f8cf59937d167567a6b644fbc838f1da4ece81753fbeef

Observation 1902d477-d63e-421a-965f-2a3ef69a73dc · 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 Retraining with Predicted Hard Labels Provably Increases Model Accuracy

Reference 259

Resolution
verified exact
arxiv_id, observed 2026-05-25T06:40:24.857529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-25T06:39:16.246591Z digest=sha256:9cebe18f6d3ae7aeb07220ce2899da4b0e703ee4d5123b91ed7b8698042a677d