Paper Citation Record · LEDGER

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods

As of 9 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 3 inbound Pith citation observations for arXiv:2506.01901.

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

pith.paper-citation-record.v1
2506.01901 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:40:35.806185Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T12:06:59.819223Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:09:41.618566Z

Reference resolution

52 of 52 outbound references displayed

verified exact3
verified fuzzy17
unresolved32
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1e2bfb6e-bbb7-4c6a-b470-40654c64c5ae · outbound

This paper cites write newline.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods write newline

Reference 1

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Observation 969956b6-1460-48f4-8a43-23eed3a28b8d · outbound

This paper cites GPT-4 Technical Report.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods GPT-4 Technical Report

Reference 2

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Observation fbe9a7a8-7549-4797-90cc-c253e3ea3f93 · outbound

This paper cites Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 3

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Observation 172ecc52-7a1f-4e7e-9dd4-2647792a79d0 · outbound

This paper cites Introducing claude, 2023.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Introducing claude, 2023

Reference 4

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Observation 3ffdb803-0a43-4efb-a25a-1ff12d9cda26 · outbound

This paper cites Ensemble of averages: Improving model selection and boosting performance in domain generalization.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Ensemble of averages: Improving model selection and boosting performance in domain generalization

Reference 5

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Observation afbcd0f9-fedc-4559-97d9-f275908eee4b · outbound

This paper cites Benign Overfitting in Linear Regression.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Benign Overfitting in Linear Regression

Reference 6

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Observation a623298b-e41b-4867-94af-3d03dbf4920d · outbound

This paper cites L., Tino, P., and Bengio, Y.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods L., Tino, P., and Bengio, Y

Reference 7

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Observation b5f4ba60-0b12-4aba-be08-e48396d088dd · outbound

This paper cites Swad: Domain generalization by seeking flat minima.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Swad: Domain generalization by seeking flat minima

Reference 8

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Observation d75cd340-413b-40c3-97d3-dddc2076a2ea · outbound

This paper cites Dna: Domain generalization with diversified neural averaging.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Dna: Domain generalization with diversified neural averaging

Reference 9

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Observation 0dd1e839-fe2d-43fe-b00e-24902ce1aecb · outbound

This paper cites Free dolly: Introducing the world's first truly open instruction-tuned llm, 2023.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Free dolly: Introducing the world's first truly open instruction-tuned llm, 2023

Reference 10

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Unresolved cited work

Reference 11

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Observation d0447fd7-df63-436b-a19a-b53ca98a6d97 · outbound

This paper cites The Llama 3 Herd of Models.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods The Llama 3 Herd of Models

Reference 12

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This paper cites and Wang, Z.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods and Wang, Z

Reference 13

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Observation 5edb5240-7d46-4122-8e92-94c8d9bcc813 · outbound

This paper cites An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Reference 14

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Unresolved cited work

Reference 15

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Observation aa270992-d5f1-4f8a-b5ba-199188ccccad · outbound

This paper cites On the Benefits of Over-parameterization for Out-of-Distribution Generalization.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods On the Benefits of Over-parameterization for Out-of-Distribution Generalization

Reference 16

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Observation 59ccb0a6-a42e-4b8e-b19f-41706396d969 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Measuring Massive Multitask Language Understanding

Reference 17

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Observation e60fce52-5f88-4bb3-9d2e-6b8c55e7eb7f · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods LoRA: Low-Rank Adaptation of Large Language Models

Reference 18

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Observation 461e2c1f-572d-4de4-a57f-cdba94f3caa3 · outbound

This paper cites Continual Learning for Text Classification with Information Disentanglement Based Regularization.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Continual Learning for Text Classification with Information Disentanglement Based Regularization

Reference 19

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Observation fd6814d4-2475-4181-aa9c-330712df7687 · outbound

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods and Lounici, K

Reference 20

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Observation 3dbb4679-0115-4db8-8568-2985ac3c12c0 · outbound

This paper cites and Vedelsby, J.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods and Vedelsby, J

Reference 21

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Observation dfe6c01c-6e39-4022-af6d-5cc5de58d595 · outbound

This paper cites Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift

Reference 22

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Observation 502ae3d2-093e-4b5f-806f-b463b3d8b825 · outbound

This paper cites Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

Reference 23

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods On the eigenvalue decay rates of a class of neural-network related kernel functions defined on general domains

Reference 24

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods and Rosasco, L

Reference 25

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Observation 96fbe0d2-f1e5-4ee2-a6ba-f0f65aecc7c0 · outbound

This paper cites Spurious Feature Diversification Improves Out-of-distribution Generalization.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Spurious Feature Diversification Improves Out-of-distribution Generalization

Reference 26

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Mitigating the Alignment Tax of RLHF

Reference 27

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Sobolev acceleration and statistical optimality for learning elliptic equations via gradient descent

Reference 28

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This paper cites Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting

Reference 29

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods and Cohen, N

Reference 30

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods and Maclin, R

Reference 31

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Unresolved cited work

Reference 32

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Ensemble based systems in decision making

Reference 33

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Observation b82ce9c2-ca8d-40cf-9f8e-0aa80296ea9d · outbound

This paper cites Diverse Weight Averaging for Out-of-Distribution Generalization.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Diverse Weight Averaging for Out-of-Distribution Generalization

Reference 34

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Observation a09aa34c-fbf8-45e0-8957-fc581aa3ef11 · outbound

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Ensemble-based classifiers

Reference 35

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Observation 3403b045-0edf-4a4f-b37d-faa9a8f72054 · outbound

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

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 36

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Gemini: A Family of Highly Capable Multimodal Models

Reference 37

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Gemma 2: Improving Open Language Models at a Practical Size

Reference 38

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Trainable projected gradient method for robust fine-tuning

Reference 39

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods High-dimensional probability: An introduction with applications in data science, volume 47

Reference 40

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A

Reference 41

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods W., Li, M., Kornblith, S., Roelofs, R., Lopes, R

Reference 42

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Qwen2 Technical Report

Reference 43

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Unresolved cited work

Reference 44

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Mathematical Analysis of Machine Learning Algorithms

Reference 45

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Why transformers need adam: A hessian perspective

Reference 46

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 47

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 48

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Ensembling neural networks: many could be better than all

Reference 49

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods @esa (Ref

Reference 50

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Unresolved cited work

Reference 51

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Unresolved cited work

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Pith citing papers

Observation 0aa9d925-374b-4faa-9840-c335b232bdbf · inbound

Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading cites this paper.

Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods

Reference 21

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Observation 6e1c9936-a366-4a10-b130-ac2da7024a4a · inbound

AesFormer: Transform Everyday Photos into Beautiful Memories cites this paper.

AesFormer: Transform Everyday Photos into Beautiful Memories Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:11:12.673045Z

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Observation 769fb3fd-cfc6-4b27-a6bd-779b8fcbf3e0 · inbound

Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition cites this paper.

Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:09:41.620182Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.