Paper Citation Record · LEDGER

(How) Can Transformers Predict Pseudo-Random Numbers?

As of 14 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 3 inbound Pith citation observations for arXiv:2502.10390.

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

pith.paper-citation-record.v1
2502.10390 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T18:32:14.852483Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T07:21:36.652240Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

verified exact3
verified fuzzy13
unresolved22
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8fa7b530-c065-4ca0-a264-6e7dfc08dc1e · outbound

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

(How) Can Transformers Predict Pseudo-Random Numbers? Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:16.296565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b08aa4bb-c2a6-4d75-9d4d-cb1962d76b72 · outbound

This paper cites What learning algorithm is in-context learning? investigations with linearmodels, 2023.

(How) Can Transformers Predict Pseudo-Random Numbers? What learning algorithm is in-context learning? investigations with linearmodels, 2023

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:16.266724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0c0f718b-a69d-49e4-98b5-eeb8c8ed6d15 · outbound

This paper cites Physics of Language Models: Part 1, Learning Hierarchical Language Structures.

(How) Can Transformers Predict Pseudo-Random Numbers? Physics of Language Models: Part 1, Learning Hierarchical Language Structures

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.414281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31faa282-0468-441f-b64f-28570381dc55 · outbound

This paper cites an unresolved cited work.

(How) Can Transformers Predict Pseudo-Random Numbers? Unresolved cited work

Reference 4

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-07T18:32:15.695347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6ccf4f19-16bc-483b-a777-17cc33e5f30a · outbound

This paper cites Towards a theory of how the structure of language is acquired by deep neural networks.

(How) Can Transformers Predict Pseudo-Random Numbers? Towards a theory of how the structure of language is acquired by deep neural networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.480461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd89f2d7-2e8e-4821-898c-ef7aee3b80bd · outbound

This paper cites M., Favero, A., and Wyart, M.

(How) Can Transformers Predict Pseudo-Random Numbers? M., Favero, A., and Wyart, M

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:16.242377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ab083dbe-98c7-4090-92e9-b823f962068e · outbound

This paper cites and Zou, D.

(How) Can Transformers Predict Pseudo-Random Numbers? and Zou, D

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:16.221968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 10da8810-b13e-4943-b9af-a0dad811fc45 · outbound

This paper cites Three models for the description of language.

(How) Can Transformers Predict Pseudo-Random Numbers? Three models for the description of language

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.535338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a754838-2723-4bb3-ba5a-0c787cd333e3 · outbound

This paper cites Neural Networks and the Chomsky Hierarchy.

(How) Can Transformers Predict Pseudo-Random Numbers? Neural Networks and the Chomsky Hierarchy

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.589213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00b1d5fe-c5ac-4cf2-ad64-6a3785f6b19e · outbound

This paper cites To grok or not to grok: Disentangling generalization and memorization on corrupted algorithmic datasets.

(How) Can Transformers Predict Pseudo-Random Numbers? To grok or not to grok: Disentangling generalization and memorization on corrupted algorithmic datasets

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:16.174417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bfb1fbbb-74cd-44c4-82c7-91221e920893 · outbound

This paper cites Grokking Modular Polynomials.

(How) Can Transformers Predict Pseudo-Random Numbers? Grokking Modular Polynomials

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.645620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a93dfc9d-b0f1-422e-9dc1-ec46d1284383 · outbound

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

(How) Can Transformers Predict Pseudo-Random Numbers? An image is worth 16x16 words: Transformers for image recognition at scale

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.654526Z

Source-reported events for the cited work

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Observation f72934fd-d4d4-452c-8c36-58fdcba7f4e4 · outbound

This paper cites an unresolved cited work.

(How) Can Transformers Predict Pseudo-Random Numbers? Unresolved cited work

Reference 13

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-07T18:32:15.400757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 48b5c80c-52dd-406b-8180-b9a55177fff7 · outbound

This paper cites Grokking modular arithmetic.

(How) Can Transformers Predict Pseudo-Random Numbers? Grokking modular arithmetic

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.671833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1822ff14-55bc-41b8-a397-ffbe84e6c0f9 · outbound

This paper cites Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks.

(How) Can Transformers Predict Pseudo-Random Numbers? Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.681010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8d0d95e-bbcd-49c1-8a79-081a3da51867 · outbound

This paper cites In-context learning creates task vectors, 2023.

(How) Can Transformers Predict Pseudo-Random Numbers? In-context learning creates task vectors, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:16.111394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9ab6c1be-48f5-492a-b06f-db520107ef7b · outbound

This paper cites an unresolved cited work.

(How) Can Transformers Predict Pseudo-Random Numbers? Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.696177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7486adb9-2044-42d1-94cc-d9e7e2914307 · outbound

This paper cites an unresolved cited work.

(How) Can Transformers Predict Pseudo-Random Numbers? Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-07T18:32:16.068709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bd64d9d6-4b0a-4a44-9997-b1701959cb1b · outbound

This paper cites Learning skillful medium-range global weather forecasting.

(How) Can Transformers Predict Pseudo-Random Numbers? Learning skillful medium-range global weather forecasting

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:16.043493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d01014ae-51c7-42dd-89aa-9df057680e2f · outbound

This paper cites In-context vectors: Making in context learning more effective and controllable through latent space steering, 2024.

(How) Can Transformers Predict Pseudo-Random Numbers? In-context vectors: Making in context learning more effective and controllable through latent space steering, 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:16.004468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 523fc187-9b37-47f4-8d21-1d6e41c6999c · outbound

This paper cites and Hutter, F.

(How) Can Transformers Predict Pseudo-Random Numbers? and Hutter, F

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.728002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3e92fce-8555-4c51-b983-84e7284a6016 · outbound

This paper cites Uncovering a Universal Abstract Algorithm for Modular Addition in Neural Networks.

(How) Can Transformers Predict Pseudo-Random Numbers? Uncovering a Universal Abstract Algorithm for Modular Addition in Neural Networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.733852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e8c3e48-40d4-4698-9263-e8f5be05db7e · outbound

This paper cites Transformers Can Do Arithmetic with the Right Embeddings.

(How) Can Transformers Predict Pseudo-Random Numbers? Transformers Can Do Arithmetic with the Right Embeddings

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.746603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19047ed8-f375-424a-b3e6-7c20c929f15d · outbound

This paper cites Progress measures for grokking via mechanistic interpretability.

(How) Can Transformers Predict Pseudo-Random Numbers? Progress measures for grokking via mechanistic interpretability

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:15.960403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation adac80fb-2b1b-46a6-914e-4501f0693095 · outbound

This paper cites In-context Learning and Induction Heads.

(How) Can Transformers Predict Pseudo-Random Numbers? In-context Learning and Induction Heads

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.762136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bdfabd4-2eb9-45a4-af80-d908929da84a · outbound

This paper cites an unresolved cited work.

(How) Can Transformers Predict Pseudo-Random Numbers? Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-07T18:32:15.930370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7c146c5c-bc2b-4634-87f1-96971df1eec3 · outbound

This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

(How) Can Transformers Predict Pseudo-Random Numbers? Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 27

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Source-reported events for the cited work

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Observation ba629857-f26f-43fa-a198-e9d821d8a3b6 · outbound

This paper cites and Wolf, L.

(How) Can Transformers Predict Pseudo-Random Numbers? and Wolf, L

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:15.909485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a51114b4-2131-428f-9ea0-1a2a066d491e · outbound

This paper cites Language models are unsupervised multitask learners.

(How) Can Transformers Predict Pseudo-Random Numbers? Language models are unsupervised multitask learners

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.793627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4162ba25-dc60-44e5-bf91-dee5b7ab7307 · outbound

This paper cites an unresolved cited work.

(How) Can Transformers Predict Pseudo-Random Numbers? Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-07T18:32:15.865259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c5ab5823-34e2-4239-b5eb-d2abb76398a7 · outbound

This paper cites Open Problems in Mechanistic Interpretability.

(How) Can Transformers Predict Pseudo-Random Numbers? Open Problems in Mechanistic Interpretability

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.806991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8eb4a528-746b-40d6-8ee4-37cd83f1de87 · outbound

This paper cites Computationally easy, spectrally good multipliers for congruential pseudorandom number generators.

(How) Can Transformers Predict Pseudo-Random Numbers? Computationally easy, spectrally good multipliers for congruential pseudorandom number generators

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-07T18:32:14.954630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 592b534e-cd7e-4c50-96bb-a59575162518 · outbound

This paper cites N., Kaiser, L., and Polosukhin, I.

(How) Can Transformers Predict Pseudo-Random Numbers? N., Kaiser, L., and Polosukhin, I

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.818842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b2cf917-6ed1-43d0-9569-ae5991714c70 · outbound

This paper cites Transformers learn in-context by gradient descent, 2023.

(How) Can Transformers Predict Pseudo-Random Numbers? Transformers learn in-context by gradient descent, 2023

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:15.815621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 113006aa-b3d3-4286-9e05-f794d5d8884e · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

(How) Can Transformers Predict Pseudo-Random Numbers? Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:14.832037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ee35d3a-eed5-4702-b084-e56ac422645c · outbound

This paper cites and Nanda, N.

(How) Can Transformers Predict Pseudo-Random Numbers? and Nanda, N

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:15.784181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0d31b312-807d-451c-b72b-94a34c2cf7eb · outbound

This paper cites The clock and the pizza: Two stories in mechanistic explanation of neural networks.

(How) Can Transformers Predict Pseudo-Random Numbers? The clock and the pizza: Two stories in mechanistic explanation of neural networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:15.758861Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6839b307-0cce-4770-ac25-614aceff560e · outbound

This paper cites write newline.

(How) Can Transformers Predict Pseudo-Random Numbers? write newline

Reference 38

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Pith citing papers

Observation 62567c29-b328-4e11-a615-8c4e5c6987ed · inbound

Learning Pseudorandom Numbers with Transformers: Permuted Congruential Generators, Curricula, and Interpretability cites this paper.

Learning Pseudorandom Numbers with Transformers: Permuted Congruential Generators, Curricula, and Interpretability (How) Can Transformers Predict Pseudo-Random Numbers?

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbfce7a2-8d98-408f-a855-a15e256dff8f · inbound

Sequential Correlations Change In-Context Learning: Effective Context Length and Architectural Mismatch cites this paper.

Sequential Correlations Change In-Context Learning: Effective Context Length and Architectural Mismatch (How) Can Transformers Predict Pseudo-Random Numbers?

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-12T00:51:41.905788Z

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Unavailable: canonical work link unavailable.

Observation 6e592903-89c8-4ab0-b674-d613d732d2a3 · inbound

Pseudorandom Streams within Diffusion Models Act as Learnable Inputs That Affect Generation Quality cites this paper.

Pseudorandom Streams within Diffusion Models Act as Learnable Inputs That Affect Generation Quality (How) Can Transformers Predict Pseudo-Random Numbers?

Reference 6

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Source-reported events for the cited work

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