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Paper Citation Record · LEDGER

Loss-to-Loss Prediction: Scaling Laws for All Datasets

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2411.12925.

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

pith.paper-citation-record.v1
2411.12925 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 2026-08-06T06:34:29.942622+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:47:51.739078Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T02:52:27.116228Z

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 301b63cf-4ca6-43db-9fd3-45446de0fef7 · inbound

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws cites this paper.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Loss-to-Loss Prediction: Scaling Laws for All Datasets

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-23T02:52:27.118254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-05-23T02:47:37.492619Z digest=sha256:fe41579e90fe2e3c147e71e4540981d0e8a2f94b0515d8aad7e4abe36fe194f7

Observation 7d601040-612c-4bf7-82fb-444c1c6d7747 · inbound

Characterization and Mitigation of Training Instabilities in Microscaling Formats cites this paper.

Characterization and Mitigation of Training Instabilities in Microscaling Formats Loss-to-Loss Prediction: Scaling Laws for All Datasets

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T22:47:51.739078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T22:47:51.739078Z digest=sha256:962ea58d716369d98a2d67d8f7462675458f14653d00d17e70534d93cdd704eb

Observation 7f6d9595-27b7-498f-bc6f-105d6625a25d · inbound

Fast and Simplex: 2-Simplicial Attention in Triton cites this paper.

Fast and Simplex: 2-Simplicial Attention in Triton Loss-to-Loss Prediction: Scaling Laws for All Datasets

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T20:31:47.679967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:31:47.679967Z digest=sha256:243ea0e04da2b30163df6b0b54251bc4ab9cf30a7f72d7955853a6416b702897

Observation 1c69b71e-f28a-42a8-bc81-ee5b518d9a9f · inbound

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

Language Models Improve When Pretraining Data Matches Target Tasks Loss-to-Loss Prediction: Scaling Laws for All Datasets

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T16:53:08.690318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:53:08.690318Z digest=sha256:24167412cab4bc125cd30c1ede9b087503d745a3cda7cf4ca18bfba147a9c7c3

Observation 390254aa-afba-40f3-a213-37590a99fb04 · inbound

Domain Fine-Tuning FinBERT on Finnish Histopathological Reports: Train-Time Signals and Downstream Correlations cites this paper.

Domain Fine-Tuning FinBERT on Finnish Histopathological Reports: Train-Time Signals and Downstream Correlations Loss-to-Loss Prediction: Scaling Laws for All Datasets

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T11:35:18.719653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-10T11:34:21.690639Z digest=sha256:275e687920dad7f58e8588456c70ce26f8802a51002001961ab420a9005e8cc3

Observation 8d93b66f-50d8-4b03-84ba-f98b0bf73032 · inbound

On the Invariance and Generality of Neural Scaling Laws cites this paper.

On the Invariance and Generality of Neural Scaling Laws Loss-to-Loss Prediction: Scaling Laws for All Datasets

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-11T03:15:56.088442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-11T02:34:14.087140Z digest=sha256:4c07a1096607146d808719979e1ae81e9fd859a40aa9f459ca8f1640e7415e1e