Paper Citation Record · LEDGER

Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2410.11820.

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

pith.paper-citation-record.v1
2410.11820 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:07:23.231892Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T21:15:09.610754Z

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 0603f12a-85fd-4fe7-8eea-fad70a913f8a · inbound

Loss-to-Loss Prediction: Scaling Laws for All Datasets cites this paper.

Loss-to-Loss Prediction: Scaling Laws for All Datasets Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T17:07:23.231892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 415bd496-4b96-4f7c-8250-33b7ef651893 · inbound

Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Grounding cites this paper.

Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Grounding Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T05:12:20.117651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdb7789a-0e56-494b-8906-9c4d1b8c0e4b · inbound

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models cites this paper.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.720797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4f68df4-6ffd-431f-8e69-55acc40d2c9d · inbound

Metadata Conditioning Accelerates Language Model Pre-training cites this paper.

Metadata Conditioning Accelerates Language Model Pre-training Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T22:21:29.553340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c682751e-f173-4956-b12a-2c77e6c2917b · inbound

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts cites this paper.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:38.235917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc341f6a-2ab7-40e0-850b-76dd6c6778da · inbound

MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training cites this paper.

MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:15:09.612941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d320334e-ef9c-49b0-aac3-8208f8a951de · inbound

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP cites this paper.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T14:35:00.356911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b311690c-0dce-4614-b305-f00efb81c32c · inbound

Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives cites this paper.

Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:33:50.960916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fb5bd19-70eb-4a72-9e70-11a649b5f5f6 · inbound

Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning cites this paper.

Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:41.369147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b381de6-1e61-4e5f-b7c1-e4c845bb4124 · inbound

Ambient Diffusion Omni: Training Good Models with Bad Data cites this paper.

Ambient Diffusion Omni: Training Good Models with Bad Data Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T05:01:14.201460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d111a8b-1cd7-4b98-8475-76012a50d654 · inbound

Language Models Improve When Pretraining Data Matches Target Tasks cites this paper.

Language Models Improve When Pretraining Data Matches Target Tasks Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T16:53:10.664013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e257326-75b0-4b0e-8204-617beb0d6ee4 · inbound

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training cites this paper.

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T11:59:52.195074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.