Paper Citation Record · LEDGER

Universal pre-training by iterated random computation

As of 22 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.20057.

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

pith.paper-citation-record.v1
2506.20057 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:04:26.625476Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact0
verified fuzzy22
unresolved17
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External citation measurements

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Outbound references

Observation 7892e3aa-a84e-4b28-82ac-56d439713c12 · outbound

This paper cites Theoretical Computer Science 354(3), 391–404 (2006).

Universal pre-training by iterated random computation Theoretical Computer Science 354(3), 391–404 (2006)

Reference 1

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Observation a6e5dcfa-5de9-4eb3-a2ff-e198d81de6ac · outbound

This paper cites Computer 49(05), 54–63 (may 2016).

Universal pre-training by iterated random computation Computer 49(05), 54–63 (may 2016)

Reference 2

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Observation 3f9dc1c6-a6cc-4c32-a607-82485525df9e · outbound

This paper cites In: International conference on algorithmic learning theory.

Universal pre-training by iterated random computation In: International conference on algorithmic learning theory

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:04:27.756265Z

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Observation c43d5654-3685-4dbe-8d2a-2d198626aa08 · outbound

This paper cites In: Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings 26.

Universal pre-training by iterated random computation In: Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings 26

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:04:27.719458Z

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Observation d7285496-6197-4800-85db-540ee6af8d3e · outbound

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Universal pre-training by iterated random computation Unresolved cited work

Reference 5

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Observation 0a951dad-6ede-4ff4-942d-f5532061ba75 · outbound

This paper cites Ihre geschichtliche Entwicklung und wirtschaftliche Seite.

Universal pre-training by iterated random computation Ihre geschichtliche Entwicklung und wirtschaftliche Seite

Reference 6

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Observation e48e0b0f-c6d6-4cd1-a20f-ba3d61c73250 · outbound

This paper cites Advances in neural information processing sys- tems 34, 28431–28441 (2021).

Universal pre-training by iterated random computation Advances in neural information processing sys- tems 34, 28431–28441 (2021)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:04:27.577435Z

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Observation 988c7736-4c98-42d4-bb11-2eb0c553ea04 · outbound

This paper cites IEEE Access 9, 51416–51431 (2021).

Universal pre-training by iterated random computation IEEE Access 9, 51416–51431 (2021)

Reference 8

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Universal pre-training by iterated random computation Unresolved cited work

Reference 9

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Observation a2934f24-13d1-4c17-84bd-87f5fae19125 · outbound

This paper cites John Wiley & Sons (1999).

Universal pre-training by iterated random computation John Wiley & Sons (1999)

Reference 10

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Observation 34dc3177-f199-44a9-b9e3-ba55e4d27ede · outbound

This paper cites The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning.

Universal pre-training by iterated random computation The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning

Reference 11

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Observation 1a3da2a5-a370-4a93-9bed-7fe72697cc69 · outbound

This paper cites ICML (2024) 28.

Universal pre-training by iterated random computation ICML (2024) 28

Reference 12

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Observation 6b6fa579-b29d-4bcb-a3c9-b01fae0f7369 · outbound

This paper cites MIT press (2007).

Universal pre-training by iterated random computation MIT press (2007)

Reference 13

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Universal pre-training by iterated random computation Unresolved cited work

Reference 14

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Observation b554d650-437a-4df9-8852-ed69b0645c0c · outbound

This paper cites Neural compu- tation 9(8), 1735–1780 (1997).

Universal pre-training by iterated random computation Neural compu- tation 9(8), 1735–1780 (1997)

Reference 15

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Observation ca126510-7b5a-47b7-bf67-456cf44c563b · outbound

This paper cites Nature 637(8045), 319–326 (2025).

Universal pre-training by iterated random computation Nature 637(8045), 319–326 (2025)

Reference 16

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Observation ff869533-f68f-4896-803f-fe4ff123b0ed · outbound

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Universal pre-training by iterated random computation Unresolved cited work

Reference 17

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Observation 58b18e4e-398b-48c0-958a-67fd98b41b30 · outbound

This paper cites Digital Communications and Networks 9(1), 79– 89 (2023).

Universal pre-training by iterated random computation Digital Communications and Networks 9(1), 79– 89 (2023)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:04:27.285056Z

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Observation fe84d037-cdda-43b1-9d2d-2f3038c7c7c3 · outbound

This paper cites In: Algorithmic Probability and Friends.

Universal pre-training by iterated random computation In: Algorithmic Probability and Friends

Reference 19

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Observation 5aafb491-ef5e-4671-8fe2-fe3e23d93085 · outbound

This paper cites The Depth-to-Width Interplay in Self-Attention.

Universal pre-training by iterated random computation The Depth-to-Width Interplay in Self-Attention

Reference 20

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Observation cfacdf18-09ec-4878-adaf-05c211f08c6a · outbound

This paper cites Decoupled Weight Decay Regularization.

Universal pre-training by iterated random computation Decoupled Weight Decay Regularization

Reference 21

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Observation 2ec485cf-acfe-4371-ab9c-f52cfeedcb53 · outbound

This paper cites In: Algorithms and complexity , pp.

Universal pre-training by iterated random computation In: Algorithms and complexity , pp

Reference 24

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Observation 136777cc-6fd0-4755-8f2b-24e9fbcb8e46 · outbound

This paper cites The Guardian https://www.theguardian.com/technology/2025/jun/11/ disney-universal-ai-lawsuit.

Universal pre-training by iterated random computation The Guardian https://www.theguardian.com/technology/2025/jun/11/ disney-universal-ai-lawsuit

Reference 25

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Observation b2683e1c-53f6-48ed-a635-8e98d1d0a242 · outbound

This paper cites Transformers Can Do Bayesian Inference.

Universal pre-training by iterated random computation Transformers Can Do Bayesian Inference

Reference 26

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This paper cites In: In- ternational Conference on Machine Learning.

Universal pre-training by iterated random computation In: In- ternational Conference on Machine Learning

Reference 27

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Observation c5a29a2d-f604-4894-adcc-3547562e06b1 · outbound

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Universal pre-training by iterated random computation Unresolved cited work

Reference 28

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This paper cites The Guardian https://www.theguardian.com/books/2023/sep/20/ authors-lawsuit-openai-george-rr-martin-john-grisham.

Universal pre-training by iterated random computation The Guardian https://www.theguardian.com/books/2023/sep/20/ authors-lawsuit-openai-george-rr-martin-john-grisham

Reference 29

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Universal pre-training by iterated random computation Unresolved cited work

Reference 30

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Observation 72d48d14-4042-4ebc-91ec-97c1f05dcbb8 · outbound

This paper cites Play and Learn: Using Video Games to Train Computer Vision Models.

Universal pre-training by iterated random computation Play and Learn: Using Video Games to Train Computer Vision Models

Reference 31

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This paper cites In: Proceedings of the fifth annual workshop on Computational learning theory.

Universal pre-training by iterated random computation In: Proceedings of the fifth annual workshop on Computational learning theory

Reference 32

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Universal pre-training by iterated random computation Unresolved cited work

Reference 33

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Observation 4b9a6ca8-681d-43b9-a319-246fadacf300 · outbound

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Universal pre-training by iterated random computation Unresolved cited work

Reference 34

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Universal pre-training by iterated random computation Unresolved cited work

Reference 35

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Observation 8961aab8-8f7f-460d-bab3-c692b0dbe367 · outbound

This paper cites Advances in neural information processing systems 30 (2017).

Universal pre-training by iterated random computation Advances in neural information processing systems 30 (2017)

Reference 36

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Observation eb7a707a-4680-44e7-80c6-477dc9db6459 · outbound

This paper cites In: The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024.

Universal pre-training by iterated random computation In: The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024

Reference 37

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Observation 9b3760d5-6792-4bc4-898d-3bafc5f53935 · outbound

This paper cites ICML (2024).

Universal pre-training by iterated random computation ICML (2024)

Reference 38

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Observation dda439b9-ae71-412b-a0a2-f2ead70aebe0 · outbound

This paper cites arXiv preprint arXiv:2405.09591 (2024).

Universal pre-training by iterated random computation arXiv preprint arXiv:2405.09591 (2024)

Reference 39

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Observation 1e9ec1ca-75f1-469f-a4e9-fe43c3dcd43f · outbound

This paper cites IEEE transactions on evolutionary computation 1(1), 67–82 (1997) 30.

Universal pre-training by iterated random computation IEEE transactions on evolutionary computation 1(1), 67–82 (1997) 30

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:04:26.925611Z

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Observation 8912acec-2869-4e77-8493-475ab3bbcfd2 · outbound

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Universal pre-training by iterated random computation Unresolved cited work

Reference 41

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Observation c4a660d1-97ef-4452-a0f8-f59325f4099c · outbound

This paper cites We clip gradients by scaling every gradient vector larger than 1 to norm 1.

Universal pre-training by iterated random computation We clip gradients by scaling every gradient vector larger than 1 to norm 1

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:04:26.886443Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

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