Paper Citation Record · LEDGER

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval

As of 10 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2502.05164.

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

pith.paper-citation-record.v1
2502.05164 v2

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:12:20.003565Z

measured 52 of 52 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T04:14:18.393326Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

51 of 51 outbound references displayed

verified exact0
verified fuzzy24
unresolved27
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 91004943-45a4-491a-8a50-1b85437a1f2e · outbound

This paper cites Transformers learn to implement preconditioned gradient descent for in-context learning.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 1

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Observation 2877f911-2b69-4805-8814-c2bb08c1b3f4 · outbound

This paper cites What learning algorithm is in-context learning? investigations with linear models.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval What learning algorithm is in-context learning? investigations with linear models

Reference 2

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Observation b6c0baf3-5fc8-421b-aabb-2ce801e5d6a2 · outbound

This paper cites Building Normalizing Flows with Stochastic Interpolants.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Building Normalizing Flows with Stochastic Interpolants

Reference 3

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This paper cites Learning patterns and pattern sequences by self-organizing nets of threshold elements.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Learning patterns and pattern sequences by self-organizing nets of threshold elements

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:12:20.798226Z

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Observation 4d8c0977-5f99-4bdb-84ac-672326f4985e · outbound

This paper cites In search of dispersed memories: Generative diffusion models are associative memory networks.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval In search of dispersed memories: Generative diffusion models are associative memory networks

Reference 5

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Observation 0224f3c6-1bb8-4b94-894e-3915ce98ea3b · outbound

This paper cites J., Gutfreund, H., and Sompolinsky, H.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval J., Gutfreund, H., and Sompolinsky, H

Reference 6

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Observation def250aa-c5fc-40f6-a925-0ce3c81078ee · outbound

This paper cites M., Castillo, I.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval M., Castillo, I

Reference 7

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Observation 8cf6c370-5ab8-4e73-aa2d-67bb43ad5a0d · outbound

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

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 8

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Observation c513ae5b-17af-4014-9276-548b8989ca39 · outbound

This paper cites Skyformer: Remodel self-attention with gaussian kernel and nyström method.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Skyformer: Remodel self-attention with gaussian kernel and nyström method

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:12:20.750265Z

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Observation 3ff74433-c413-4b49-a643-c5be97014610 · outbound

This paper cites M., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval M., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J

Reference 10

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Observation 13b5fa6c-41bf-43a1-8b11-d7a44164c3ca · outbound

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

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 11

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Observation 3264bd70-f400-4df2-9ce7-81f0e6ae447b · outbound

This paper cites On a model of associative memory with huge storage capacity.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval On a model of associative memory with huge storage capacity

Reference 12

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Observation aa2f8aff-c2cd-4c57-aa5a-25f824b86ae7 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 13

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Observation b61d8712-34c0-433e-81c8-e426144f182d · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval An image is worth 16x16 words: Transformers for image recognition at scale

Reference 14

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Unresolved cited work

Reference 15

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Observation e311b2af-b77d-4843-8f8d-9661f34e8b51 · outbound

This paper cites What Can Transformers Learn In-Context? A Case Study of Simple Function Classes.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval What Can Transformers Learn In-Context? A Case Study of Simple Function Classes

Reference 16

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Observation 769bf4ae-bd6c-4187-9632-39bdccf7310e · outbound

This paper cites Sampling with flows, diffusion, and autoregressive neural networks from a spin-glass perspective.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Sampling with flows, diffusion, and autoregressive neural networks from a spin-glass perspective

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:12:20.659317Z

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Observation cfc00335-dc6e-407c-bf5c-92308aee9f33 · outbound

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Unresolved cited work

Reference 18

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Observation f07c8e26-3afb-48ab-b07d-3139de192457 · outbound

This paper cites Probability inequalities for sums of bounded random variables.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Probability inequalities for sums of bounded random variables

Reference 19

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval H., Zaki, M., and Krotov, D

Reference 20

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval H., Zaki, M

Reference 21

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Observation a510701a-a36a-413f-8e47-a9b94bf78bca · outbound

This paper cites H., Strobelt, H., Ram, P., and Krotov, D.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval H., Strobelt, H., Ram, P., and Krotov, D

Reference 22

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Observation a11133fd-e21e-40ec-890a-9eb59bec96b3 · outbound

This paper cites Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models

Reference 23

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Unresolved cited work

Reference 24

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Y.-C., Yang, D., Wu, D., Xu, C., Chen, B.-Y., and Liu, H

Reference 25

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This paper cites Transformers are rnns: fast autoregressive transformers with linear attention.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Transformers are rnns: fast autoregressive transformers with linear attention

Reference 26

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval A new frontier for hopfield networks

Reference 27

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval and Hopfield, J

Reference 28

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval and Hopfield, J

Reference 29

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Probability theory i

Reference 31

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval and M\'ezard, M

Reference 32

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This paper cites Universal hopfield networks: A general framework for single-shot associative memory models.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Universal hopfield networks: A general framework for single-shot associative memory models

Reference 33

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Observation 970f2bd5-9310-4143-9bbe-c3408fa135b5 · outbound

This paper cites Associatron-a model of associative memory.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Associatron-a model of associative memory

Reference 34

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval J., Ambrogioni, L., and Krotov, D

Reference 35

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval P., Kopp, M

Reference 36

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Observation 5eb13efe-e8cc-40f4-b31c-d6163978bcf3 · outbound

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

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval The mechanistic basis of data dependence and abrupt learning in an in-context classification task

Reference 37

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval High-Dimensional Statistics

Reference 38

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Unresolved cited work

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval and Zilman, A

Reference 41

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval LLaMA: Open and Efficient Foundation Language Models

Reference 42

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval and Kolter, J

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:12:20.317383Z

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval N., Łukasz Kaiser, and Polosukhin, I

Reference 44

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Observation f203ca04-c781-45fb-b34a-0e80a415dbc0 · outbound

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Transformers learn in-context by gradient descent

Reference 45

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Y.-C., Hsiao, T.-Y., and Liu, H

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:12:20.272991Z

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Observation 17a42cd6-5863-467e-a1a3-180683bd7195 · outbound

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction

Reference 47

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Unresolved cited work

Reference 48

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Observation 5754da27-3fab-401a-9095-c25a02eca7e7 · outbound

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In-context denoising with one-layer transformers: connections between attention and associative memory retrieval @esa (Ref

Reference 49

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Reference 51

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Pith citing papers

Observation 674a9e47-8fd6-4ebc-9cee-50ab974d3e94 · inbound

Muon in Associative Memory Learning: Training Dynamics and Scaling Laws cites this paper.

Muon in Associative Memory Learning: Training Dynamics and Scaling Laws In-context denoising with one-layer transformers: connections between attention and associative memory retrieval

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