Paper Citation Record · LEDGER

Is In-Context Universality Enough? MLPs are Also Universal In-Context

As of 10 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 3 inbound Pith citation observations for arXiv:2502.03327.

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

pith.paper-citation-record.v1
2502.03327 v1

Coverage vector

measured 77 of 77 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T05:16:20.035595Z

measured 80 of 80 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:12:59.142441Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T17:51:09.301070Z

Reference resolution

77 of 77 outbound references displayed

verified exact8
verified fuzzy39
unresolved30
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ecd161e9-20ed-4c02-a585-230745130e54 · outbound

This paper cites Designing universal causal deep learning models: The geometric (hyper) transformer.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Designing universal causal deep learning models: The geometric (hyper) transformer

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.819157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2e5cce64-3d67-4e93-83ca-b598f340edde · outbound

This paper cites What learning algorithm is in-context learning.

Is In-Context Universality Enough? MLPs are Also Universal In-Context What learning algorithm is in-context learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.808759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8fba3083-7a91-4e99-b703-c71fc1540334 · outbound

This paper cites Linear extension operators between spaces of lipschitz maps and optimal transport.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Linear extension operators between spaces of lipschitz maps and optimal transport

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.799372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9fa33873-5f97-4cd6-9068-f66fb392769d · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Neural Machine Translation by Jointly Learning to Align and Translate

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.806166Z

Source-reported events for the cited work

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Observation adf248e8-b3d7-41f4-987a-71c3c16af205 · outbound

This paper cites Transformers as statisticians: Provable in-context learning with in-context algorithm selection.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Transformers as statisticians: Provable in-context learning with in-context algorithm selection

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.809865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cd71b23-f8dc-4404-a3d4-97898b6b37ff · outbound

This paper cites Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.784267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation feeabac3-24f9-4d28-9a86-8a1850822636 · outbound

This paper cites Introduction to linear optimization, volume 6.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Introduction to linear optimization, volume 6

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.774597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0e2fad36-95a0-485d-9258-41c165a87a77 · outbound

This paper cites Optimal approximation with sparsely connected deep neural networks.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Optimal approximation with sparsely connected deep neural networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.764993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dfdbcd61-8efb-4286-a046-20056e333561 · outbound

This paper cites Neural Spacetimes for DAG Representation Learning.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Neural Spacetimes for DAG Representation Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.823871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bde4a029-d612-4d49-bf3f-51c9fd72862a · outbound

This paper cites Scalable message passing neural networks: No need for attention in large graph representation learning.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Scalable message passing neural networks: No need for attention in large graph representation learning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-08-09T05:16:21.320430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 892765f6-02ea-48fa-b67e-3b80ad089871 · outbound

This paper cites Bridson and Andr\'e Haefliger.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Bridson and Andr\'e Haefliger

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.830006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42031319-43a2-4125-a996-d1e5ef36b978 · outbound

This paper cites an unresolved cited work.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Unresolved cited work

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0f94750a-5847-4d67-8bf1-9d3e708cc52c · outbound

This paper cites How smooth is attention? In ICML 2024, 2024.

Is In-Context Universality Enough? MLPs are Also Universal In-Context How smooth is attention? In ICML 2024, 2024

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.755302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a7ddefc3-7877-40e9-98f9-c5866659a0df · outbound

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

Is In-Context Universality Enough? MLPs are Also Universal In-Context Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.839437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0e48b21-816d-487d-89a0-2bc734aa533c · outbound

This paper cites Efficient approximation of high-dimensional functions with neural networks.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Efficient approximation of high-dimensional functions with neural networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.745626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7890448d-ed45-46c6-81f9-056bbde011c7 · outbound

This paper cites Efficient approximation of high-dimensional functions with neural networks.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Efficient approximation of high-dimensional functions with neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.735121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c30dd528-9bc7-45e6-99cc-eeb3aacf4200 · outbound

This paper cites Tighter bounds on the expressivity of transformer encoders.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Tighter bounds on the expressivity of transformer encoders

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.726168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 654f77b9-665a-465e-92b0-ce0acc3388ed · outbound

This paper cites Conditional positional encodings for vision transformers.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Conditional positional encodings for vision transformers

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.717383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 41e8b8ed-08b1-4230-bd01-1bd8ee1a9f46 · outbound

This paper cites Global universal approximation of functional input maps on weighted spaces.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Global universal approximation of functional input maps on weighted spaces

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.854213Z

Source-reported events for the cited work

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Observation 8ef7685d-2ea1-4c50-bc62-296db87fbc89 · outbound

This paper cites The density theorem and hausdorff inequality for packing measure in general metric spaces.

Is In-Context Universality Enough? MLPs are Also Universal In-Context The density theorem and hausdorff inequality for packing measure in general metric spaces

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.708952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7acec2cb-a88b-4fad-b2ed-924cf48f165a · outbound

This paper cites Neural snowflakes: Universal latent graph inference via trainable latent geometries.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Neural snowflakes: Universal latent graph inference via trainable latent geometries

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.699002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ae0ca787-76c9-4307-ab80-230dc6ab1875 · outbound

This paper cites Finite Sample Analysis and Bounds of Generalization Error of Gradient Descent in In-Context Linear Regression.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Finite Sample Analysis and Bounds of Generalization Error of Gradient Descent in In-Context Linear Regression

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-09T05:16:20.965615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ae49969e-d74e-4443-8975-fc6694ab48df · outbound

This paper cites Attention Enables Zero Approximation Error.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Attention Enables Zero Approximation Error

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-09T05:16:20.952402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 763f1c62-3955-45bc-8fc5-a24c1d2fc811 · outbound

This paper cites Simultaneously solving fbsdes with neural operators of logarithmic depth, constant width, and sub-linear rank.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Simultaneously solving fbsdes with neural operators of logarithmic depth, constant width, and sub-linear rank

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-08-09T05:16:20.938514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ef6a8941-78ac-46b6-9612-a0f811e67e68 · outbound

This paper cites Globally injective and bijective neural operators.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Globally injective and bijective neural operators

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-09T05:16:20.750418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 70cbcf87-ee94-4be2-a11c-3c090bc819ec · outbound

This paper cites Transformers are Universal In-context Learners.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Transformers are Universal In-context Learners

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.876202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fdb0272-03fc-433a-920a-561efe06fa05 · outbound

This paper cites What can transformers learn in-context? a case study of simple function classes.

Is In-Context Universality Enough? MLPs are Also Universal In-Context What can transformers learn in-context? a case study of simple function classes

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.879073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca50433c-27a1-46e9-9f54-7c53c8047e6b · outbound

This paper cites Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.882193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6309ba7d-88d5-4133-98aa-54665326a09b · outbound

This paper cites A survey on lipschitz-free banach spaces.

Is In-Context Universality Enough? MLPs are Also Universal In-Context A survey on lipschitz-free banach spaces

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.683727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 73a2262f-1616-499a-b2b4-6658d8fb5883 · outbound

This paper cites Can a transformer represent a kalman filter? In 6th Annual Learning for Dynamics & Control Conference, pages 1502--1512.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Can a transformer represent a kalman filter? In 6th Annual Learning for Dynamics & Control Conference, pages 1502--1512

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.673484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1cd76a4b-0353-4272-9d09-2262fd606861 · outbound

This paper cites Multilayer feedforward networks are universal approximators.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Multilayer feedforward networks are universal approximators

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.893083Z

Source-reported events for the cited work

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Observation 4ae1ce36-5e72-4925-9045-3e7307451acc · outbound

This paper cites Addressing common misinterpretations of kart and uat in neural network literature.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Addressing common misinterpretations of kart and uat in neural network literature

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.896042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 503268a5-baf7-433c-8b82-1f2dc3d21549 · outbound

This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Neural tangent kernel: Convergence and generalization in neural networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.899075Z

Source-reported events for the cited work

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Observation f9610677-c401-4ecb-be63-c32f987129fe · outbound

This paper cites arvenp\"a\.

Is In-Context Universality Enough? MLPs are Also Universal In-Context arvenp\"a\

Reference 34

Resolution: verified exact; doi, observed 2026-08-09T05:16:20.124856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a120df26-5b1d-4234-8641-31796ed7966a · outbound

This paper cites Universal approximation with deep narrow networks.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Universal approximation with deep narrow networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.651911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cc5daf97-93f1-4978-b8e5-b220328d06ff · outbound

This paper cites Transformers provably solve parity efficiently with chain of thought.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Transformers provably solve parity efficiently with chain of thought

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.642513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a86836f6-562c-4349-84bc-b59962c9e274 · outbound

This paper cites Transformers learn nonlinear features in context.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Transformers learn nonlinear features in context

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.633115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 017484ad-b5de-47df-a479-80afe0fe4ebb · outbound

This paper cites Transformers are minimax optimal nonparametric in-context learners.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Transformers are minimax optimal nonparametric in-context learners

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.623175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 730d4adf-5091-4045-b56b-80320bf4be82 · outbound

This paper cites Wasserstein-2 Generative Networks.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Wasserstein-2 Generative Networks

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.918262Z

Source-reported events for the cited work

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Observation 43eb5b28-bbe7-41c8-87a2-f9dfc56d72a1 · outbound

This paper cites Neural optimal transport.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Neural optimal transport

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.613866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc6a46ec-65a4-4e79-afa7-a6a649eeb39c · outbound

This paper cites Universal approximation theorems for differentiable geometric deep learning.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Universal approximation theorems for differentiable geometric deep learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.604277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6a2d62d8-7e82-40e9-b2e0-f8254c245e8c · outbound

This paper cites Universal approximation under constraints is possible with transformers.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Universal approximation under constraints is possible with transformers

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.594315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6a3a28d0-cb4b-4915-a578-df76a177c2be · outbound

This paper cites An Approximation Theory for Metric Space-Valued Functions With A View Towards Deep Learning.

Is In-Context Universality Enough? MLPs are Also Universal In-Context An Approximation Theory for Metric Space-Valued Functions With A View Towards Deep Learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.931454Z

Source-reported events for the cited work

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Observation 24ae4e24-6654-4c0c-9e6c-c61e8574efed · outbound

This paper cites Learnable fourier features for multi-dimensional spatial positional encoding.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Learnable fourier features for multi-dimensional spatial positional encoding

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.585087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c5928e98-64fd-4828-bbbd-916d34fe444c · outbound

This paper cites Transformers as algorithms: Generalization and stability in in-context learning.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Transformers as algorithms: Generalization and stability in in-context learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.577097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8edd86e1-bb9b-4c92-b7ad-66d400f53683 · outbound

This paper cites Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.939842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b935a897-753c-4941-87bd-7e7d205f5be1 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Is In-Context Universality Enough? MLPs are Also Universal In-Context KAN: Kolmogorov-Arnold Networks

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.942568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38d62c41-b84f-41fd-a94b-abc8fe94f379 · outbound

This paper cites Asymptotic theory of in-context learning by linear attention.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Asymptotic theory of in-context learning by linear attention

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.945962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72120011-5fe1-45af-a491-9d8d0d8752cc · outbound

This paper cites Your transformer may not be as powerful as you expect.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Your transformer may not be as powerful as you expect

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.568838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 20a5e2fa-22a0-4f2e-b3df-9aafc6e9a2b1 · outbound

This paper cites Every complete doubling metric space carries a doubling measure.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Every complete doubling metric space carries a doubling measure

Reference 50

Resolution: verified exact; doi, observed 2026-08-09T05:16:20.113744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2f6a6133-168b-46fa-91d4-bf4fafd15447 · outbound

This paper cites The Expressive Power of Transformers with Chain of Thought.

Is In-Context Universality Enough? MLPs are Also Universal In-Context The Expressive Power of Transformers with Chain of Thought

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.956712Z

Source-reported events for the cited work

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Observation f001226e-3fb4-4c26-a24c-ca1f71fe6bdb · outbound

This paper cites Length independent pac-bayes bounds for simple rnns.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Length independent pac-bayes bounds for simple rnns

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.560918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cd91476d-b53a-48d2-adde-ec661e5f586f · outbound

This paper cites In-context Learning and Induction Heads.

Is In-Context Universality Enough? MLPs are Also Universal In-Context In-context Learning and Induction Heads

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.962376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4874534-0991-4286-8350-d962373f4ffc · outbound

This paper cites Equivalence of approximation by convolutional neural networks and fully-connected networks.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Equivalence of approximation by convolutional neural networks and fully-connected networks

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.550874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0cb4c701-24ff-407c-8ead-2e7a46585d73 · outbound

This paper cites Mathematical theory of deep learning.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Mathematical theory of deep learning

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.967807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b6a6ac9-f566-472d-b88a-d6fc4b47704b · outbound

This paper cites Universal in-context approximation by prompting fully recurrent models.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Universal in-context approximation by prompting fully recurrent models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.541237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0d42cb49-e56d-4671-bfc0-74e0cf90fdcd · outbound

This paper cites Computational optimal transport: With applications to data science.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Computational optimal transport: With applications to data science

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.530362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8e6e71c5-3e4b-4944-aec1-bb395100c4eb · outbound

This paper cites Computational optimal transport: With applications to data science.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Computational optimal transport: With applications to data science

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.520140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b6828b66-1d34-44a2-b68a-592eeee36696 · outbound

This paper cites Searching for Activation Functions.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Searching for Activation Functions

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.979626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 576ece54-d6d1-4102-a301-efc2767c9d30 · outbound

This paper cites The mechanistic basis of data dependence and abrupt learning in an in-context classification task.

Is In-Context Universality Enough? MLPs are Also Universal In-Context The mechanistic basis of data dependence and abrupt learning in an in-context classification task

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.510196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation df979d68-0b12-4bb0-b3bb-f5d44cd9fca2 · outbound

This paper cites Singular value perturbation and deep network optimization.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Singular value perturbation and deep network optimization

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.498449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c37f7e34-d5b8-4939-8923-ea02a711f7c7 · outbound

This paper cites GLU Variants Improve Transformer.

Is In-Context Universality Enough? MLPs are Also Universal In-Context GLU Variants Improve Transformer

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.992077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 660c996d-274f-4b18-bf08-86700d39d9d7 · outbound

This paper cites Nonparametric estimation of non-crossing quantile regression process with deep requ neural networks.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Nonparametric estimation of non-crossing quantile regression process with deep requ neural networks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.487880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9794ae5b-a703-4c69-be27-8f5e0d9fd6f8 · outbound

This paper cites Optimal approximation rate of R e LU networks in terms of width and depth.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Optimal approximation rate of R e LU networks in terms of width and depth

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:19.997348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 132396c5-5537-4aeb-aca4-731d31a94432 · outbound

This paper cites Expressivity of Spiking Neural Networks.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Expressivity of Spiking Neural Networks

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:20.000075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fbf6c71-5356-48ba-9775-66a27610e31e · outbound

This paper cites Training dynamics of multi-head softmax attention for in-context learning: Emergence, convergence, and optimality.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Training dynamics of multi-head softmax attention for in-context learning: Emergence, convergence, and optimality

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.476539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0bea5d22-66d0-4bc5-84f7-7ebf5cae3563 · outbound

This paper cites What formal languages can transformers express? a survey.

Is In-Context Universality Enough? MLPs are Also Universal In-Context What formal languages can transformers express? a survey

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.464500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 316656c7-333d-43f9-a7d4-e444ff40f0af · outbound

This paper cites Adaptivity of deep relu network for learning in besov and mixed smooth besov spaces: optimal rate and curse of dimensionality.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Adaptivity of deep relu network for learning in besov and mixed smooth besov spaces: optimal rate and curse of dimensionality

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.453428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 30d5222c-6ab3-4ac8-8861-a8483b674e97 · outbound

This paper cites an unresolved cited work.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Unresolved cited work

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:20.012867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2625e20-e81b-4c6c-945e-627c2856885f · outbound

This paper cites Attention is all you need.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Attention is all you need

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:20.015931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24995316-9db7-483e-81c8-3def793f395a · outbound

This paper cites Optimal transport, volume 338 of Grundlehren der mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences].

Is In-Context Universality Enough? MLPs are Also Universal In-Context Optimal transport, volume 338 of Grundlehren der mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences]

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:20.018499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 427b4ef2-0682-4cf6-a99d-c50dba969da0 · outbound

This paper cites Distance-based classification with lipschitz functions.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Distance-based classification with lipschitz functions

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.437598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dbddc119-e52e-4b17-aec2-3e6fc19cdbbd · outbound

This paper cites Transformers learn in-context by gradient descent.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Transformers learn in-context by gradient descent

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:20.024110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 711b7090-9459-4e12-bcd5-669dc2b9e48f · outbound

This paper cites Lipschitz algebras.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Lipschitz algebras

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:20.026715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3b20c69-2960-4595-b1f8-9f2e8cb91a65 · outbound

This paper cites Optimal approximation of continuous functions by very deep relu networks.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Optimal approximation of continuous functions by very deep relu networks

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.409922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c428d000-6644-46e0-8aca-40f216afa3d1 · outbound

This paper cites Trained transformers learn linear models in-context.

Is In-Context Universality Enough? MLPs are Also Universal In-Context Trained transformers learn linear models in-context

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:16:21.388303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 70821f6a-6323-4e15-b203-24cd5a429a5c · outbound

This paper cites In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization.

Is In-Context Universality Enough? MLPs are Also Universal In-Context In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-09T05:16:20.035595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 176dbf16-26da-40ad-8009-8b4d397fc23b · inbound

Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data cites this paper.

Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data Is In-Context Universality Enough? MLPs are Also Universal In-Context

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-05T13:12:59.142441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb347cb6-80a6-497b-b45a-f7c4fa54372e · 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 Is In-Context Universality Enough? MLPs are Also Universal In-Context

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:25.486024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07a816b1-f254-4ad2-8a7d-725c13fe7c22 · inbound

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

Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning Is In-Context Universality Enough? MLPs are Also Universal In-Context

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:51:09.303503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.