Paper Citation Record · LEDGER

Rethinking Data Selection for Supervised Fine-Tuning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.06094.

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

pith.paper-citation-record.v1
2402.06094 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 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:38:36.853572Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T03:26:29.400520Z

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 4030d25d-179f-431b-ac80-90581dfa3cbe · inbound

LLaVA-CoT: Let Vision Language Models Reason Step-by-Step cites this paper.

LLaVA-CoT: Let Vision Language Models Reason Step-by-Step Rethinking Data Selection for Supervised Fine-Tuning

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-16T11:35:25.958124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 380a5976-f76b-4e3e-aaea-3f1b5d27e457 · inbound

Boosting LLM via Learning from Data Iteratively and Selectively cites this paper.

Boosting LLM via Learning from Data Iteratively and Selectively Rethinking Data Selection for Supervised Fine-Tuning

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.853572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffdf4a28-a334-4d60-81b2-e1c4a5dff4df · inbound

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence cites this paper.

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence Rethinking Data Selection for Supervised Fine-Tuning

Reference 123

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T22:23:15.850556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c774c1ef-8849-42ac-92f9-4cccabe91f17 · inbound

Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning with Real-Time Feedback cites this paper.

Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning with Real-Time Feedback Rethinking Data Selection for Supervised Fine-Tuning

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:50:54.449235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5f5393b7-3159-4ae3-835a-0dfb2a0ebd08 · inbound

RIZZ: Routing Interactions to Near Zero-Interference Zones for Continual Adaptation of Black-Box Agents cites this paper.

RIZZ: Routing Interactions to Near Zero-Interference Zones for Continual Adaptation of Black-Box Agents Rethinking Data Selection for Supervised Fine-Tuning

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:26:29.402191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.