Paper Citation Record · LEDGER

Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2508.11953.

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

pith.paper-citation-record.v1
2508.11953 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:11:02.133177Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 111e95f3-43c0-47a8-8f13-77ceac3ef911 · inbound

Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning cites this paper.

Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T16:54:27.921929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59aa6f80-54ff-4a90-8364-1a9dd8dc955f · inbound

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications cites this paper.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:20.733283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ab9a158-d575-43ff-89cb-55bd71b48300 · inbound

Bridging Compute- and Data-Optimal Pretraining cites this paper.

Bridging Compute- and Data-Optimal Pretraining Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models

Reference 98

Resolution: verified exact; local_arxiv, observed 2026-08-01T03:08:35.560127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 222a1d89-64c8-40b8-97dc-ec19b59e88fe · inbound

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning cites this paper.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T17:11:02.133177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.