Paper Citation Record · LEDGER

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws

As of 19 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 7 inbound Pith citation observations for arXiv:2502.12120.

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

pith.paper-citation-record.v1
2502.12120 v3

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-23T02:47:37.492619Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:32:12.517211Z

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

54 of 54 outbound references displayed

verified exact34
verified fuzzy8
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch12

External citation measurements

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

Outbound references

Observation e36ddbe5-0711-4b57-bb6e-f18d93399fa1 · outbound

This paper cites Exploring The Landscape of Distributional Robustness for Question Answering Models.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Exploring The Landscape of Distributional Robustness for Question Answering Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.150102Z

Source-reported events for the cited work

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Observation c7cea62b-03fc-4c28-b0ed-fea36cc1fd3b · outbound

This paper cites Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T02:52:27.153689Z

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Observation d06baeb1-d551-4cc1-b11b-31006ccf408e · outbound

This paper cites PIQA: Reasoning about Physical Commonsense in Natural Language.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T02:52:27.142978Z

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Observation 6aace21d-bf84-4ef1-8893-bdbb6402fbe7 · outbound

This paper cites Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:27:39.098836Z

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Observation e2ddf806-0f71-4c3b-aaff-ea600b6737c1 · outbound

This paper cites GPT-NeoX-20B: An Open-Source Autoregressive Language Model.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws GPT-NeoX-20B: An Open-Source Autoregressive Language Model

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T02:52:27.125494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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

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

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 2026-08-19T06:32:44.657259+00:00.

Observation fbdb04fe-61c0-4e19-8fdf-1792bcc767d6 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.121737Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 363b004a-1ffb-4daf-bc06-6e9c0cfe343d · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.128976Z

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Observation 084b1a00-7f2d-49f3-b08d-4b2d743be9d5 · outbound

This paper cites Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.135687Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1b5153ba-a9e3-4f99-8924-8726896eb432 · outbound

This paper cites Understanding Emergent Abilities of Language Models from the Loss Perspective.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Understanding Emergent Abilities of Language Models from the Loss Perspective

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-23T02:52:27.109203Z

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Observation 364ec55a-d6e0-462f-9dfc-01d21d19c0c5 · outbound

This paper cites Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP).

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.114874Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a75009f9-5d6a-4cf2-b1ff-5b5c1803bba2 · outbound

This paper cites Language models scale reliably with over-training and on downstream tasks.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Language models scale reliably with over-training and on downstream tasks

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.106052Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4cf67fdd-c60e-4d83-a24f-b30aa6f3fc25 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.098574Z

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Observation cda4c4ca-532c-4873-b0c1-0a612c7ca51d · outbound

This paper cites He, B., Yin, L., Zhen, H.-L., Liu, S., Wu, H., Zhang, X., Yuan, M., and Ma, C.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws He, B., Yin, L., Zhen, H.-L., Liu, S., Wu, H., Zhang, X., Yuan, M., and Ma, C

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-23T02:52:27.102560Z

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Observation 81d5c581-4990-43f7-abd9-756b2bcf6f4f · outbound

This paper cites S., Kozareva, Z., and Roemmele, M.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws S., Kozareva, Z., and Roemmele, M

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:27:39.108621Z

Source-reported events for the cited work

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Observation d1538cda-641c-40ad-a48e-900c7d8de0d8 · outbound

This paper cites The Llama 3 Herd of Models.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws The Llama 3 Herd of Models

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.111926Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 25ad0d65-43b7-4e46-b480-ad677be85622 · outbound

This paper cites OLM o: Accelerating the science of language models.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws OLM o: Accelerating the science of language models

Reference 17

Resolution: verified exact; doi, observed 2026-05-23T02:52:26.337254Z

Source-reported events for the cited work

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Observation fcc93f31-4bbd-486f-bd71-73b88014a7ff · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.139211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c8dc67b7-acfa-4e7b-ac8c-aae2bacf76c8 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Measuring Massive Multitask Language Understanding

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.084002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 58e3fb3e-fadf-4442-90c2-2ea0e79218b3 · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Deep Learning Scaling is Predictable, Empirically

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.146660Z

Source-reported events for the cited work

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Observation 6b70340b-707e-4ee4-955a-5fa53d4ac107 · outbound

This paper cites Training Compute-Optimal Large Language Models.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Training Compute-Optimal Large Language Models

Reference 21

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T02:52:27.077172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 629d0798-9ebd-46e2-9a83-6010ee3ac1ed · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 22

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T02:52:27.094710Z

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Observation 078c30a2-9096-4c4c-b0c7-95988dbad9d3 · outbound

This paper cites Scaling laws for downstream task performance of large language models.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Scaling laws for downstream task performance of large language models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.087842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fbae82ac-aae8-499b-8a10-f6fdc2595eda · outbound

This paper cites Scaling Laws for Neural Language Models.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Scaling Laws for Neural Language Models

Reference 24

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T02:52:27.090961Z

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Observation d936a0bf-0910-4dc8-a2fe-0c7c5e64c97a · outbound

This paper cites nanogpt.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws nanogpt

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:27:39.115411Z

Source-reported events for the cited work

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Observation c358675d-1e29-4408-b9ce-8ada8219276c · outbound

This paper cites Adam: A Method for Stochastic Optimization.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Adam: A Method for Stochastic Optimization

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.132399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8d3247ff-e86f-4033-97c3-2edf744f7ba8 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.073705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2df8dae7-c30f-4812-b7d4-9205bce9803e · outbound

This paper cites Decoupled Weight Decay Regularization.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Decoupled Weight Decay Regularization

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.059391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0b1e145f-0b0c-4cbf-938e-5aa28133c018 · outbound

This paper cites Quantifying Variance in Evaluation Benchmarks.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Quantifying Variance in Evaluation Benchmarks

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.051526Z

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Observation eb592648-1229-459d-983a-d2cd8215eae8 · outbound

This paper cites Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.055543Z

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Observation eb1ea2a2-7574-4ae8-80ef-4b181286e205 · outbound

This paper cites In Search of Forgotten Domain Generalization.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws In Search of Forgotten Domain Generalization

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.063822Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d0ac1c9a-2327-4dd7-88ea-771f6bac3209 · outbound

This paper cites Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.067063Z

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Observation fe364a94-f893-4468-ab85-1d61454841d2 · outbound

This paper cites The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.008419Z

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Observation 16246f4b-6745-49a5-8b97-5e93b98c865f · outbound

This paper cites The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

Reference 34

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T02:52:27.040547Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 67dd0421-289c-416f-89ea-365cd9570f7d · outbound

This paper cites Resolving Discrepancies in Compute-Optimal Scaling of Language Models.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Resolving Discrepancies in Compute-Optimal Scaling of Language Models

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.048190Z

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Observation 85eaf020-83ab-46d9-9930-5a1b8df51300 · outbound

This paper cites Language models are unsupervised multitask learners.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Language models are unsupervised multitask learners

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:27:39.118336Z

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Observation 18ec24e3-6e4c-4537-9c4c-9f0d19ccef1e · outbound

This paper cites On Linear Identifiability of Learned Representations.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws On Linear Identifiability of Learned Representations

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.025945Z

Source-reported events for the cited work

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Observation 95f63f9d-15ea-4528-9fec-3598a2e9c887 · outbound

This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 38

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T02:52:27.029445Z

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Observation db2ab083-36a2-4b7f-a885-cb06416fa40b · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws SocialIQA: Commonsense Reasoning about Social Interactions

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.036979Z

Source-reported events for the cited work

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Observation 24b30f4c-6ab0-4dd5-8bb9-fb22db13d759 · outbound

This paper cites On the Inductive Bias of Stacking Towards Improving Reasoning.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws On the Inductive Bias of Stacking Towards Improving Reasoning

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.044287Z

Source-reported events for the cited work

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Observation 7068dbf6-0708-4ce8-8de9-7d697bdceed7 · outbound

This paper cites Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.070448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a9c4ac4a-f922-4458-8966-c4436408edf6 · outbound

This paper cites SlimPajama-DC: Understanding Data Combinations for LLM Training.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws SlimPajama-DC: Understanding Data Combinations for LLM Training

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-05-23T02:52:27.015453Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8d15554c-d71a-4ef7-8c1c-fdf1cccd2c44 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.022377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 66c487a5-5417-4570-8154-86fe5729766b · outbound

This paper cites CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.018724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aff85cf0-1cc3-48ad-9117-65df32209e0c · outbound

This paper cites Measuring Robustness to Natural Distribution Shifts in Image Classification.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Measuring Robustness to Natural Distribution Shifts in Image Classification

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.011832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91de4426-7f01-48ba-ad49-6892bf231d93 · outbound

This paper cites Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.033438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9387ca80-436e-48bc-9825-6b3459f90f8c · outbound

This paper cites Y., Haziza, D., Wehrstedt, L., Copet, J., Teytaud, O., and Lopez-Paz, D.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Y., Haziza, D., Wehrstedt, L., Copet, J., Teytaud, O., and Lopez-Paz, D

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:27:39.112060Z

Source-reported events for the cited work

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Observation 5e3d9051-8da0-4201-b34a-7055c581d87e · outbound

This paper cites Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St´ efan J.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St´ efan J

Reference 48

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This paper cites and Komatsuzaki, A.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws and Komatsuzaki, A

Reference 49

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Observation 73e00f2a-6b80-4a37-9734-c94fd4c1993a · outbound

This paper cites Scaling Laws Across Model Architectures: A Comparative Analysis of Dense and MoE Models in Large Language Models.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Scaling Laws Across Model Architectures: A Comparative Analysis of Dense and MoE Models in Large Language Models

Reference 50

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Observation ad57ad34-54a2-4996-b754-2a1c99fdde05 · outbound

This paper cites Pretraining frequency predicts compositional generalization of CLIP on real-world tasks.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Pretraining frequency predicts compositional generalization of CLIP on real-world tasks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:27:39.102181Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 749b280b-6aec-42a7-afbe-1319b694d4a1 · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 52

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Observation 7ade2323-c95b-4630-ae9f-567be4c1e44d · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.001197Z

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Observation 7ae70e27-f2c3-4dd1-8a0b-762fbf1f3215 · outbound

This paper cites write newline.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws write newline

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:27:39.121286Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 89f4ec0a-15e4-410c-9400-07e39d7e2ef8 · inbound

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection cites this paper.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws

Reference 30

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Observation 7fcf85e1-298e-4c01-8cb1-422e54e41ba0 · inbound

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining cites this paper.

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws

Reference 35

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Observation 1846d484-bc65-4f03-a5e3-cebc18861ac0 · inbound

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

Language Models Improve When Pretraining Data Matches Target Tasks LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws

Reference 64

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Observation 3c516a2c-cfca-4dda-b51e-369d1236d499 · inbound

MentisOculi: Revealing the Limits of Reasoning with Mental Imagery cites this paper.

MentisOculi: Revealing the Limits of Reasoning with Mental Imagery LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws

Reference 33

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Observation 9833058b-abf7-4bad-b952-1a77eea611cc · inbound

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks cites this paper.

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T17:31:48.273075Z

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Observation e59d2c6f-c038-46e0-af4a-c47f3d7bce59 · inbound

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

Bridging Compute- and Data-Optimal Pretraining LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws

Reference 103

Resolution: metadata mismatch; local_arxiv, observed 2026-08-01T03:08:35.287832Z

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Observation b4333c37-c8e1-46c5-88ff-8055cf3b5f51 · inbound

LittleLearner: Language Models Under Pedagogically Controlled Knowledge Exposure cites this paper.

LittleLearner: Language Models Under Pedagogically Controlled Knowledge Exposure LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T04:32:12.517211Z

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