Paper Citation Record · LEDGER

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes

As of 17 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2504.12991.

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

pith.paper-citation-record.v1
2504.12991 v2

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:27:30.549363Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T01:18:31.661827Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T01:21:58.041298Z

Reference resolution

52 of 52 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4abcc00c-5c43-44b7-8882-99572ad414b0 · outbound

This paper cites GPT-4 Technical Report.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes GPT-4 Technical Report

Reference 1

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Observation aa921b7c-2f42-40fb-acef-bbb8a5826a7b · outbound

This paper cites Invariant Risk Minimization.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Invariant Risk Minimization

Reference 2

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Observation a1ab9c69-1142-4536-bed4-c8ccf6fd0c9a · outbound

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Unresolved cited work

Reference 3

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This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 4

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Observation c6009500-d3ac-4df5-b0fe-fa336749bb87 · outbound

This paper cites Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure

Reference 5

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Observation 88d89878-ba81-413a-9428-11e13a2b4f76 · outbound

This paper cites Joint distribution optimal transportation for domain adaptation.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Joint distribution optimal transportation for domain adaptation

Reference 6

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Observation d6948229-1c31-4cb3-8646-c1c649e8e6be · outbound

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Unresolved cited work

Reference 7

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Observation 9e89c951-ba94-4072-a692-d0c190d9066e · outbound

This paper cites A pac-bayesian approach for domain adaptation with specialization to linear classifiers.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes A pac-bayesian approach for domain adaptation with specialization to linear classifiers

Reference 8

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Observation 8f893a9c-d6c5-45b0-88fa-35e6f09f9c19 · outbound

This paper cites Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change

Reference 9

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This paper cites and Andriushchenko, M.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes and Andriushchenko, M

Reference 10

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Observation 7e7e6a25-1bf8-460c-84e0-a39ce1caaa6a · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 11

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Observation 082a9449-a5dc-4b71-93d7-aad941f98a28 · outbound

This paper cites Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods

Reference 12

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Observation 0b8c7803-dca8-4028-82cb-dff6b8f67cce · outbound

This paper cites Large Language and Reasoning Models are Shallow Disjunctive Reasoners.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Large Language and Reasoning Models are Shallow Disjunctive Reasoners

Reference 13

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Observation ad4d1dd7-fb8f-4347-982f-edc355ee97e7 · outbound

This paper cites Detecting change in data streams.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Detecting change in data streams

Reference 14

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Observation 0dd020c5-b869-4f73-93e0-89363120ef78 · outbound

This paper cites Transformers Provably Solve Parity Efficiently with Chain of Thought.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Transformers Provably Solve Parity Efficiently with Chain of Thought

Reference 15

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Observation 67554582-45df-4d4b-bcf9-a6952a679488 · outbound

This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes A simple unified framework for detecting out-of-distribution samples and adversarial attacks

Reference 16

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Observation c7c79e20-81ab-452a-9b04-425cc2007592 · outbound

This paper cites Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis

Reference 17

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Observation 9d3cefa5-de29-473e-908e-96a20a050db7 · outbound

This paper cites Universal representation learning from multiple domains for few-shot classification.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Universal representation learning from multiple domains for few-shot classification

Reference 18

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Observation 6b7647d3-b693-483b-9825-daa70a97b990 · outbound

This paper cites Energy-based out-of-distribution detection.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Energy-based out-of-distribution detection

Reference 19

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Observation b394e2e8-b366-49e4-9969-c6127a77aca9 · outbound

This paper cites Transdrift: Modeling word-embedding drift using transformer.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Transdrift: Modeling word-embedding drift using transformer

Reference 20

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Observation a57fc58c-ee5a-48b1-b2af-d4397dcc0ed7 · outbound

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Domain adaptation with multiple sources

Reference 21

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Observation 6af1c331-92b1-470f-89fc-dc42dcd2e95c · outbound

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Unresolved cited work

Reference 22

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Observation 06d3668f-c4fb-4736-9223-5781a98d51fa · outbound

This paper cites Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?.

A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 23

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Observation bf1077b5-f112-401d-9e26-e6ae6379d060 · outbound

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Towards a statistical theory of learning to learn in-context with transformers

Reference 24

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Observation c6253800-d8e5-4839-8d51-f9a8614f19c9 · outbound

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Unresolved cited work

Reference 25

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Observation ff3bde54-31fa-4d4a-afb3-c9b8a483b0cf · outbound

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages

Reference 26

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Observation 25cc9cc3-6a6c-48bb-a0b6-a38154cb43ab · outbound

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Language models are unsupervised multitask learners

Reference 27

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Unresolved cited work

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes M., and Zhang, M

Reference 29

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Theoretical analysis of domain adaptation with optimal transport

Reference 30

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes J., Fertig, E., Snoek, J., Poplin, R., Depristo, M., Dillon, J., and Lakshminarayanan, B

Reference 31

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 32

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes A., and Ommer, B

Reference 33

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes J., de Rezende Rocha, A., Sapkota, A., and Boult, T

Reference 34

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Wasserstein distance guided representation learning for domain adaptation

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes LLaMA: Open and Efficient Foundation Language Models

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes C., Murino, V., and Savarese, S

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Can In-context Learning Really Generalize to Out-of-distribution Tasks?

Reference 38

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Beyond in-distribution success: Scaling curves of cot granularity for language model generalization

Reference 39

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes V., Zhou, D., et al

Reference 40

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes N., Li, W., Ba, J., Grosse, R

Reference 41

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Zero-shot learning-the good, the bad and the ugly

Reference 42

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes A Theory for Length Generalization in Learning to Reason

Reference 43

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 44

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Chain-of-thought provably enables learning the (otherwise) unlearnable

Reference 45

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective

Reference 46

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Unveiling the mechanisms of explicit cot training: How chain-of-thought enhances reasoning generalization

Reference 47

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Revisiting out-of-distribution robustness in nlp: Benchmarks, analysis, and llms evaluations

Reference 48

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Evaluating interpolation and extrapolation performance of neural retrieval models

Reference 49

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Observation 253f18f7-9350-4bae-a156-b0d32b02cdfd · outbound

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes Theoretically principled trade-off between robustness and accuracy

Reference 50

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes OPT: Open Pre-trained Transformer Language Models

Reference 51

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A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization

Reference 52

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Pith citing papers

Observation a74e54a9-818c-4b5b-9933-eda9337352ea · inbound

Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens cites this paper.

Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes

Reference 15

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