Paper Citation Record · LEDGER

RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2412.14922.

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

pith.paper-citation-record.v1
2412.14922 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 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:03:47.372014Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T09:07:47.678025Z

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 bbfc070e-c4aa-4838-bdd6-01e020769a5f · inbound

We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems cites this paper.

We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:36.601660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 846414ef-cf03-4a4a-aedd-a8c1d6700dba · inbound

Analyzing the Effect of Noise in LLM Fine-tuning cites this paper.

Analyzing the Effect of Noise in LLM Fine-tuning RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T10:46:06.322096Z

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 bcfb58e6-b720-4515-b943-fdb22285099d · inbound

REALM: Reliable Expertise-Aware Language Model Fine-Tuning from Noisy Annotations cites this paper.

REALM: Reliable Expertise-Aware Language Model Fine-Tuning from Noisy Annotations RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:56:47.722829Z

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 4cf05deb-28c0-4c0f-b254-e0339f208445 · inbound

Common-agency Games for Multi-Objective Test-Time Alignment cites this paper.

Common-agency Games for Multi-Objective Test-Time Alignment RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response

Reference 90

Resolution: verified exact; arxiv_id, observed 2026-05-15T06:15:06.384504Z

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 3bb3b865-d0bb-41cf-ab23-a81aef22a942 · inbound

Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching cites this paper.

Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-07-03T09:07:47.679503Z

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 42707746-db80-41b5-92e1-f78f8e1d47ae · inbound

Positive-Unlabeled Preference Optimization For Chest X-ray Report Generation cites this paper.

Positive-Unlabeled Preference Optimization For Chest X-ray Report Generation RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T15:03:47.372014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.