Paper Citation Record · LEDGER

Does your data spark joy? Performance gains from domain upsampling at the end of training

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2406.03476.

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

pith.paper-citation-record.v1
2406.03476 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:19:58.308118Z

measured 1 of 1 external citation measurements

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

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

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4b726d53-b9a7-4127-8c27-be1661a25895 · inbound

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters cites this paper.

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:16:23.871304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 467abb9f-7e07-48f1-9caa-70d1911afc01 · inbound

Sparse Upcycling: Inference Inefficient Finetuning cites this paper.

Sparse Upcycling: Inference Inefficient Finetuning Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T21:22:21.968196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 659a3b3d-41c5-458b-888d-a765d68b6739 · inbound

The Zamba2 Suite: Technical Report cites this paper.

The Zamba2 Suite: Technical Report Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T15:04:38.090863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f125af7e-c2cb-4f1d-a55a-23303004c79e · inbound

Predicting Emergent Capabilities by Finetuning cites this paper.

Predicting Emergent Capabilities by Finetuning Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T13:41:46.008379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21b24c9f-bd47-4fc1-94be-9f92fd042679 · inbound

Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining cites this paper.

Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T12:34:16.582789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9908adf4-4096-4377-88ab-dfefffa41465 · inbound

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model cites this paper.

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 153

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:30:02.919309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d7f67488-3497-43c0-ac96-5969e1a0362f · inbound

Nemotron-CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training cites this paper.

Nemotron-CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T12:19:58.308118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e7ade0a-6bfe-48a3-9f91-44de13f1efe8 · inbound

Trillion 7B Technical Report cites this paper.

Trillion 7B Technical Report Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:05.839964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49e8d926-f6a0-448f-9652-59849d6ca6eb · inbound

Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report cites this paper.

Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T05:53:58.298711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85b25a7e-3f1d-495e-8096-3eae7485d039 · inbound

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

Language Models Improve When Pretraining Data Matches Target Tasks Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T16:53:08.641036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74aa8a65-a88f-4f57-9561-59f90caca0d5 · inbound

Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning cites this paper.

Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T14:20:37.130182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f6c5223-f9ce-42a6-b4ba-59cf61fbcb53 · 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 Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T11:59:52.159243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1035626-9902-496a-8b1b-58858639b199 · inbound

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation cites this paper.

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T17:21:06.619163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10471077-be80-4b32-8815-0f9a3c91879a · inbound

Capacity-Aware Mixture Law Enables Efficient LLM Data Optimization cites this paper.

Capacity-Aware Mixture Law Enables Efficient LLM Data Optimization Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:25:55.364976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 204710eb-0e69-413c-a64c-10445399cc8d · inbound

XekRung Technical Report cites this paper.

XekRung Technical Report Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 131

Resolution: verified exact; arxiv_id, observed 2026-05-09T20:58:06.753264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 99f9b5a6-5603-49a0-a6b5-dad88d268d48 · inbound

ZAYA1-8B Technical Report cites this paper.

ZAYA1-8B Technical Report Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T17:26:05.244182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 09f8cd8d-2ed9-45bc-add9-9ad360d989f0 · inbound

ZONOS2 Technical Report cites this paper.

ZONOS2 Technical Report Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T18:40:03.309291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2c859aab-7803-4f7e-9145-6dbf68aa80d6 · inbound

ZONOS2 Technical Report cites this paper.

ZONOS2 Technical Report Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T18:15:59.001887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 87328ef2-e8f0-430c-a537-27814a2311e2 · inbound

ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution cites this paper.

ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution Does your data spark joy? Performance gains from domain upsampling at the end of training

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T09:51:50.318054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.