Paper Citation Record · LEDGER

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws

As of 18 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2505.06699.

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

pith.paper-citation-record.v1
2505.06699 v3

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:44:57.932021Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

verified exact5
verified fuzzy16
unresolved16
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dac47b21-3710-4cf3-b8f6-09a70b047520 · outbound

This paper cites Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models

Reference 1

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Source-reported events for the cited work

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Observation e97ab280-0d2b-4e1f-9d01-a5cb521ef208 · outbound

This paper cites Theory of classification: A survey of some recent advances.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Theory of classification: A survey of some recent advances

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.426500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 09283d06-f554-499d-8f7d-bcdd64ba1b7e · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.417042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ceb85770-b602-406f-8283-6133fa8b183b · outbound

This paper cites Net2net: Accelerating learning via knowledge transfer.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Net2net: Accelerating learning via knowledge transfer

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.407297Z

Source-reported events for the cited work

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Observation 19745f1f-0396-4037-9ca8-48f4951a53f9 · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Reproducible scaling laws for contrastive language-image learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.397875Z

Source-reported events for the cited work

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Observation f5f8c17d-0570-4495-ba99-85cbcc5a48e2 · outbound

This paper cites DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Reference 6

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Source-reported events for the cited work

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Observation bb4db724-2319-472c-aa2c-ae2941b46825 · outbound

This paper cites Stacking your transformers: A closer look at model growth for efficient LLM pre-training.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Stacking your transformers: A closer look at model growth for efficient LLM pre-training

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.388032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0dde2620-8ebe-4dd3-8678-cc2d3cddacd2 · outbound

This paper cites The Llama 3 Herd of Models.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws The Llama 3 Herd of Models

Reference 8

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Source-reported events for the cited work

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Observation b590cd07-336e-49de-b29c-59a66bfea7ec · outbound

This paper cites Variance-based regularization with convex objectives.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Variance-based regularization with convex objectives

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:44:57.830479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b06b6320-b339-46b3-9ed6-cd7c1097dc87 · outbound

This paper cites C., Glynn, P.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws C., Glynn, P

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T22:44:57.835647Z

Source-reported events for the cited work

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Observation 64ffaa26-2dd8-42ed-8444-c8e1b87c6441 · outbound

This paper cites an unresolved cited work.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:44:58.378190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6a6b6b8a-2cf9-4f61-9c51-a74322da41ef · outbound

This paper cites an unresolved cited work.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Unresolved cited work

Reference 12

Resolution: verified exact; doi, observed 2026-08-15T22:44:58.002535Z

Source-reported events for the cited work

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Observation 9b7624bb-a71a-4e60-9fe6-a13b0542d97d · outbound

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Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Unresolved cited work

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1ccfefe7-1934-4966-a451-10e9c92ef814 · outbound

This paper cites M., Jain, A., Schmidt, L., Toshev, A.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws M., Jain, A., Schmidt, L., Toshev, A

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.358760Z

Source-reported events for the cited work

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Observation 2fe2998d-5089-4bb0-8076-193a26fb1b18 · outbound

This paper cites an unresolved cited work.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Unresolved cited work

Reference 15

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Source-reported events for the cited work

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Observation 0a67973f-9b05-4733-b5bc-d494f00e6264 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Distilling the Knowledge in a Neural Network

Reference 16

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Source-reported events for the cited work

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Observation c73ebc02-b65f-410a-9e1c-925704f109c5 · outbound

This paper cites Non-smooth weakly-convex finite-sum coupled compositional optimization.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Non-smooth weakly-convex finite-sum coupled compositional optimization

Reference 17

Resolution: verified exact; raw_fallback, observed 2026-08-15T22:44:58.105976Z

Source-reported events for the cited work

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Observation 04341935-4a7d-4be5-82f1-00a35d6f20ae · outbound

This paper cites Information theory and statistics.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Information theory and statistics

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.340094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 152f0ad5-bc16-4a04-a712-4cdbdf9de603 · outbound

This paper cites C., and Sidford, A.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws C., and Sidford, A

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.330250Z

Source-reported events for the cited work

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Observation 729f5d8a-dc27-482b-a5c0-397198c0ef6e · outbound

This paper cites Not all tokens are what you need for pretraining.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Not all tokens are what you need for pretraining

Reference 20

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Source-reported events for the cited work

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Observation 6425a83f-2d64-4e0f-9b3c-2950bb50b3a7 · outbound

This paper cites When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 21

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Source-reported events for the cited work

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Observation 2696bbee-3aec-42df-adac-8db90edecfb9 · outbound

This paper cites M., Razzak, M.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws M., Razzak, M

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.314733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 46bf8174-a67c-4546-bf5b-fa4d9f07b868 · outbound

This paper cites Stochastic constrained DRO with a complexity independent of sample size.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Stochastic constrained DRO with a complexity independent of sample size

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.304381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 99825f66-8f7b-4769-8594-6bd23183d355 · outbound

This paper cites Attentional-biased stochastic gradient descent.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Attentional-biased stochastic gradient descent

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.292836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 19b3cc58-e186-4822-bd74-87da356f4b6e · outbound

This paper cites Knowledge inheritance for pre-trained language models.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Knowledge inheritance for pre-trained language models

Reference 25

Resolution: verified exact; doi, observed 2026-08-15T22:44:57.991164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 41f6b338-22f8-4153-8047-bb54d06c987c · outbound

This paper cites Not all semantics are created equal: Contrastive self-supervised learning with automatic temperature individualization.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Not all semantics are created equal: Contrastive self-supervised learning with automatic temperature individualization

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.282633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4ed161ca-70aa-404e-9d72-e90b028a953f · outbound

This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I

Reference 27

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Source-reported events for the cited work

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Observation 0c545544-7af8-452c-bdf5-f2c728ecb814 · outbound

This paper cites LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 28

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Source-reported events for the cited work

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Observation 9925a759-d726-452e-94a1-c36ce81444ec · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.267024Z

Source-reported events for the cited work

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Observation ed0bbac7-c77f-486d-bea8-ee0ec5ab3476 · outbound

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Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Unresolved cited work

Reference 30

Resolution: verified exact; doi, observed 2026-08-15T22:44:57.979953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 61d5dc55-9231-47fa-b996-37e8e1b60fa3 · outbound

This paper cites an unresolved cited work.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Unresolved cited work

Reference 31

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Source-reported events for the cited work

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Observation e28a1542-3d17-4212-adf9-ded7be05b743 · outbound

This paper cites A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:44:57.969456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e30dc796-e3df-4992-9fd6-960b17a427fc · outbound

This paper cites T., Greengard, P., Karlinsky, L., Feris, R., Cox, D.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws T., Greengard, P., Karlinsky, L., Feris, R., Cox, D

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.245243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a1ad8742-427d-48ed-97c6-049f0e9cc753 · outbound

This paper cites FastCLIP: A Suite of Optimization Techniques to Accelerate CLIP Training with Limited Resources.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws FastCLIP: A Suite of Optimization Techniques to Accelerate CLIP Training with Limited Resources

Reference 34

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Source-reported events for the cited work

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Observation 62ed8068-d030-415f-a3e5-46619bbe477f · outbound

This paper cites M., Pham, H., Dong, X., Du, N., Liu, H., Lu, Y., Liang, P.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws M., Pham, H., Dong, X., Du, N., Liu, H., Lu, Y., Liang, P

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.235261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e0744fa5-d308-4616-ac8a-cf9529e16e11 · outbound

This paper cites Provable stochastic optimization for global contrastive learning: Small batch does not harm performance.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws Provable stochastic optimization for global contrastive learning: Small batch does not harm performance

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:44:58.224295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 985cacbd-ea70-48bb-8100-bdac1798d7ad · outbound

This paper cites write newline.

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws write newline

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T22:44:57.932021Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.