Paper Citation Record · LEDGER

Transformers Meet In-Context Learning: A Universal Approximation Theory

As of 9 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 4 inbound Pith citation observations for arXiv:2506.05200.

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

pith.paper-citation-record.v1
2506.05200 v2

Coverage vector

measured 71 of 71 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:33:43.583162Z

measured 75 of 75 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:37:03.120800Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T18:16:08.441338Z

Reference resolution

71 of 71 outbound references displayed

verified exact3
verified fuzzy12
unresolved56
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d5756bde-b5fa-41e3-923b-f958f71adb11 · outbound

This paper cites an unresolved cited work.

Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:33:55.944380Z

Source-reported events for the cited work

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Observation c4791222-49d1-4918-ad9a-c40cd690b27e · outbound

This paper cites In-Context Learning through the Bayesian Prism.

Transformers Meet In-Context Learning: A Universal Approximation Theory In-Context Learning through the Bayesian Prism

Reference 2

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Source-reported events for the cited work

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Observation ebfa6d0d-c284-4f6f-86ad-0ac0999ddef6 · outbound

This paper cites an unresolved cited work.

Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 3

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Source-reported events for the cited work

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Observation af3cad9a-9c2d-40aa-b2bc-4a6ca7a155d5 · outbound

This paper cites an unresolved cited work.

Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 4

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Observation 12df6de3-32cb-4a94-9ee2-47bda072fa46 · outbound

This paper cites an unresolved cited work.

Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 5

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Observation 0bb54b47-709b-4200-869a-aea5a84c1e58 · outbound

This paper cites an unresolved cited work.

Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:33:54.622669Z

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Observation b92b35dc-2b55-4b64-9e9e-bc291d271082 · outbound

This paper cites an unresolved cited work.

Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 7

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Source-reported events for the cited work

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Observation c0315443-97c5-488d-aa61-9eb901f822c2 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Transformers Meet In-Context Learning: A Universal Approximation Theory On the Opportunities and Risks of Foundation Models

Reference 8

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Source-reported events for the cited work

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Observation 810f3f2f-3b94-44a1-9b0f-e49281788331 · outbound

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

Transformers Meet In-Context Learning: A Universal Approximation Theory D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 9

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Source-reported events for the cited work

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Observation 388e0509-e66b-4298-9249-2133d8f3c0a7 · outbound

This paper cites an unresolved cited work.

Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 10

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Observation 0892a673-8ad9-4bb9-8ba8-6a3aa63cfb42 · outbound

This paper cites Theoretical limitations of multi-layer Transformer.

Transformers Meet In-Context Learning: A Universal Approximation Theory Theoretical limitations of multi-layer Transformer

Reference 11

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Source-reported events for the cited work

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Observation f5fee61a-8b01-4b22-9bcd-6d619cbfed03 · outbound

This paper cites Provably learning a multi-head attention layer.

Transformers Meet In-Context Learning: A Universal Approximation Theory Provably learning a multi-head attention layer

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T10:33:35.509713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0a96b15-0d08-4d63-bc77-d4a28a323726 · outbound

This paper cites Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality.

Transformers Meet In-Context Learning: A Universal Approximation Theory Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 13

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 14

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Source-reported events for the cited work

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Observation 79c13986-8394-4593-9150-bf46538b97e0 · outbound

This paper cites In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning.

Transformers Meet In-Context Learning: A Universal Approximation Theory In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning

Reference 15

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Observation b3a12ade-1d1c-4bba-88ea-c2f473cbaf23 · outbound

This paper cites an unresolved cited work.

Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 16

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Source-reported events for the cited work

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Observation f530a241-cb5d-45f9-98f0-db8dbcbee486 · outbound

This paper cites Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers.

Transformers Meet In-Context Learning: A Universal Approximation Theory Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 17

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Source-reported events for the cited work

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Observation 163f0a49-dd20-418e-8ed8-3f02dcd64ef9 · outbound

This paper cites A Survey on In-context Learning.

Transformers Meet In-Context Learning: A Universal Approximation Theory A Survey on In-context Learning

Reference 18

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Unavailable: canonical work link unavailable.

Observation 0b99c245-123c-410f-8583-561c978693a2 · outbound

This paper cites an unresolved cited work.

Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 19

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Observation cd031c9f-c9f9-4cef-9652-b76ba5e6d44d · outbound

This paper cites an unresolved cited work.

Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 20

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Source-reported events for the cited work

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 21

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Observation ea8b35f6-b77f-4c72-b656-4e69559601b0 · outbound

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

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Observation a86f70b7-d493-4a70-b8e7-08bfe6638851 · outbound

This paper cites Transformers are Universal In-context Learners.

Transformers Meet In-Context Learning: A Universal Approximation Theory Transformers are Universal In-context Learners

Reference 23

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Observation e1778cd7-776d-44a2-b4f6-d6ce20544564 · outbound

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Transformers Meet In-Context Learning: A Universal Approximation Theory S., and Valiant, G

Reference 24

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Observation d2b57ba7-1169-4bde-b63c-29214f0db64b · outbound

This paper cites D., and Papailiopoulos, D.

Transformers Meet In-Context Learning: A Universal Approximation Theory D., and Papailiopoulos, D

Reference 25

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Observation d9332bc3-54f7-44cc-b35e-e8692ef160ff · outbound

This paper cites How Well Can Transformers Emulate In-context Newton's Method?.

Transformers Meet In-Context Learning: A Universal Approximation Theory How Well Can Transformers Emulate In-context Newton's Method?

Reference 26

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 27

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Observation 338501c5-07b0-4f2c-bb91-6733092aa031 · outbound

This paper cites an unresolved cited work.

Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:33:51.745107Z

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Observation e22aa380-d783-4701-a7f7-717470198891 · outbound

This paper cites an unresolved cited work.

Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:33:51.413886Z

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Observation 9f785aa8-d926-4dc7-9d86-a4c56edf096d · outbound

This paper cites A Theory of Emergent In-Context Learning as Implicit Structure Induction.

Transformers Meet In-Context Learning: A Universal Approximation Theory A Theory of Emergent In-Context Learning as Implicit Structure Induction

Reference 30

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This paper cites Automatic Domain Adaptation by Transformers in In-Context Learning.

Transformers Meet In-Context Learning: A Universal Approximation Theory Automatic Domain Adaptation by Transformers in In-Context Learning

Reference 31

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Observation aa480a26-5864-4d8e-abc9-50a299814699 · outbound

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 32

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Observation ebcf0c52-8b4a-4d04-add6-475b4b27a292 · outbound

This paper cites an unresolved cited work.

Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:33:50.836283Z

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Observation afc9ab70-f704-4574-893f-1d734d7f1da5 · outbound

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

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This paper cites Repeat After Me: Transformers are Better than State Space Models at Copying.

Transformers Meet In-Context Learning: A Universal Approximation Theory Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 35

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

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Observation 1aebd610-238e-463b-84a4-83249de8afed · outbound

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Transformers Meet In-Context Learning: A Universal Approximation Theory and Nemirovski, A

Reference 37

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Observation bfa7db19-7f14-458d-a440-12bd2fe5b961 · outbound

This paper cites W., Khan, F.

Transformers Meet In-Context Learning: A Universal Approximation Theory W., Khan, F

Reference 38

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Transformers Meet In-Context Learning: A Universal Approximation Theory and Suzuki, T

Reference 39

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Observation 89ea7339-dbf9-46af-bc11-722083408b57 · outbound

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Transformers Meet In-Context Learning: A Universal Approximation Theory and Sanguineti, M

Reference 40

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Observation c4835d13-b760-4fd5-acd4-ac23b81024eb · outbound

This paper cites Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective.

Transformers Meet In-Context Learning: A Universal Approximation Theory Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective

Reference 41

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Observation cb4e955d-2b26-49d8-b1fd-66571b9508f4 · outbound

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 42

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Observation 769b032a-cfef-4f3b-a127-20b83ec0a2c7 · outbound

This paper cites How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?.

Transformers Meet In-Context Learning: A Universal Approximation Theory How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?

Reference 43

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This paper cites E., Papailiopoulos, D., and Oymak, S.

Transformers Meet In-Context Learning: A Universal Approximation Theory E., Papailiopoulos, D., and Oymak, S

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:33:48.561985Z

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Observation 252fc36b-cfc5-49c8-b26e-c7905db6cbc6 · outbound

This paper cites Chain of Thought Empowers Transformers to Solve Inherently Serial Problems.

Transformers Meet In-Context Learning: A Universal Approximation Theory Chain of Thought Empowers Transformers to Solve Inherently Serial Problems

Reference 45

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Transformers Meet In-Context Learning: A Universal Approximation Theory On the Expressive Power of Self-Attention Matrices

Reference 46

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 47

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Observation 5dcb5bf9-8a9d-41b7-b476-5a3842da069c · outbound

This paper cites Transformers Learn Shortcuts to Automata.

Transformers Meet In-Context Learning: A Universal Approximation Theory Transformers Learn Shortcuts to Automata

Reference 48

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Transformers Meet In-Context Learning: A Universal Approximation Theory V., Hashimoto, T., and Ma, T

Reference 49

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Observation d51488a1-14bc-4584-af63-2231e31a1371 · outbound

This paper cites and Sabharwal, A.

Transformers Meet In-Context Learning: A Universal Approximation Theory and Sabharwal, A

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:33:47.810104Z

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Observation b1d28389-1062-44a3-a420-23a8c71dc702 · outbound

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 51

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Observation 53aa29ae-0560-4d55-8149-b1ba7873f3df · outbound

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 52

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Observation 128c3e7c-3215-4e32-89a8-0250edc712fc · outbound

This paper cites On the Turing Completeness of Modern Neural Network Architectures.

Transformers Meet In-Context Learning: A Universal Approximation Theory On the Turing Completeness of Modern Neural Network Architectures

Reference 53

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This paper cites Transformers, parallel computation, and logarithmic depth.

Transformers Meet In-Context Learning: A Universal Approximation Theory Transformers, parallel computation, and logarithmic depth

Reference 54

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Source-reported events for the cited work

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Observation 4dd2153d-149f-4753-a89d-08e60392fa59 · outbound

This paper cites J., and Telgarsky, M.

Transformers Meet In-Context Learning: A Universal Approximation Theory J., and Telgarsky, M

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:33:46.949332Z

Source-reported events for the cited work

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

Observation 8d5e45e7-3fd9-45e1-86b8-697fac049349 · outbound

This paper cites W., Khan, M.

Transformers Meet In-Context Learning: A Universal Approximation Theory W., Khan, M

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:33:46.679478Z

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Transformers Meet In-Context Learning: A Universal Approximation Theory Do pretrained Transformers Learn In-Context by Gradient Descent?

Reference 57

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 58

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Transformers Meet In-Context Learning: A Universal Approximation Theory N., Kaiser, ., and Polosukhin, I

Reference 59

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 60

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Reference 61

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Observation 9be8aa75-a760-4b53-82bb-997f94b33d6b · outbound

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 62

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Observation 6fdb14e6-8e63-428b-9224-93dcb347cd95 · outbound

This paper cites Uncovering mesa-optimization algorithms in Transformers.

Transformers Meet In-Context Learning: A Universal Approximation Theory Uncovering mesa-optimization algorithms in Transformers

Reference 63

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 64

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This paper cites RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval.

Transformers Meet In-Context Learning: A Universal Approximation Theory RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval

Reference 65

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 66

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Transformers Meet In-Context Learning: A Universal Approximation Theory M., Raghunathan, A., Liang, P., and Ma, T

Reference 67

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Observation ef54cc4a-1763-4073-b8af-7f62ff900332 · outbound

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Transformers Meet In-Context Learning: A Universal Approximation Theory In-Context Learning with Representations: Contextual Generalization of Trained Transformers

Reference 68

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Transformers Meet In-Context Learning: A Universal Approximation Theory Self-Attention Networks Can Process Bounded Hierarchical Languages

Reference 69

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Transformers Meet In-Context Learning: A Universal Approximation Theory Unresolved cited work

Reference 70

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Observation c08623d9-6cd5-4f39-bbdc-f2d3c585938f · outbound

This paper cites What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization.

Transformers Meet In-Context Learning: A Universal Approximation Theory What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T10:33:43.583162Z

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Pith citing papers

Observation 60e3553d-05d9-4500-b701-c28612b2ca6c · inbound

Provable Low-Frequency Bias of In-Context Learning of Representations cites this paper.

Provable Low-Frequency Bias of In-Context Learning of Representations Transformers Meet In-Context Learning: A Universal Approximation Theory

Reference 2016

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Observation 4dc62d54-8731-4f56-9dc1-fb9f73ec4ef6 · inbound

How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off cites this paper.

How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off Transformers Meet In-Context Learning: A Universal Approximation Theory

Reference 2013

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Observation 677bea9f-f6cd-4ea4-a160-4711237c95a3 · inbound

Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning cites this paper.

Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning Transformers Meet In-Context Learning: A Universal Approximation Theory

Reference 29

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Observation 5c8533d3-a68a-4ac5-ab57-f636746f132f · inbound

Understanding In-Context Learning for Nonlinear Regression with Transformers: Attention as Featurizer cites this paper.

Understanding In-Context Learning for Nonlinear Regression with Transformers: Attention as Featurizer Transformers Meet In-Context Learning: A Universal Approximation Theory

Reference 20

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