Paper Citation Record · LEDGER

Training-Free Universal Approximation by Prompting Random Transformers

As of 13 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2608.09558.

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

pith.paper-citation-record.v1
2608.09558 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:14:28.528461Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

21 of 21 outbound references displayed

verified exact4
verified fuzzy5
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 55bdee17-4ebd-4de6-90e6-e47b798fc584 · outbound

This paper cites Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li, Yong Qin, Ruiqi Sun, Xin Zhou, Enzhi Wang, and Xiaohang Dong.

Training-Free Universal Approximation by Prompting Random Transformers Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li, Yong Qin, Ruiqi Sun, Xin Zhou, Enzhi Wang, and Xiaohang Dong

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:14:28.908493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3dc08dc5-b260-4b3b-b1b3-bd717fc15f60 · outbound

This paper cites URLhttps://aclanthology.org/2024.naacl-long.228/.

Training-Free Universal Approximation by Prompting Random Transformers URLhttps://aclanthology.org/2024.naacl-long.228/

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:14:28.896232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 319692d9-0506-4982-9f2d-6efd7f23bbf1 · outbound

This paper cites Prompt Engineering Through the Lens of Optimal Control.

Training-Free Universal Approximation by Prompting Random Transformers Prompt Engineering Through the Lens of Optimal Control

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T15:14:28.489885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 520f5c59-ddea-4682-a1f2-4c902019a962 · outbound

This paper cites Memory Limitations of Prompt Tuning in Transformers.

Training-Free Universal Approximation by Prompting Random Transformers Memory Limitations of Prompt Tuning in Transformers

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:14:28.643858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c3e154bd-b875-46b0-aa18-f66063b44914 · outbound

This paper cites A Theoretical Framework for Prompt Engineering: Approximating Smooth Functions with Transformer Prompts.

Training-Free Universal Approximation by Prompting Random Transformers A Theoretical Framework for Prompt Engineering: Approximating Smooth Functions with Transformer Prompts

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T15:14:28.497840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75638d22-d25f-4541-883f-66d04e1ded28 · outbound

This paper cites neurips.cc/paper_files/paper/2008/file/0efe32849d230d7f53049ddc4a4b0c60-Paper.pdf.

Training-Free Universal Approximation by Prompting Random Transformers neurips.cc/paper_files/paper/2008/file/0efe32849d230d7f53049ddc4a4b0c60-Paper.pdf

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:14:28.883476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cfc1bd5f-4143-43f9-92e9-56aafa9fea4f · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

Training-Free Universal Approximation by Prompting Random Transformers A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T15:14:28.509353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0377090-656d-436b-bfcd-370965a8d63a · outbound

This paper cites Sheng Shen, Alexei Baevski, Ari S.

Training-Free Universal Approximation by Prompting Random Transformers Sheng Shen, Alexei Baevski, Ari S

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:14:28.870448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ab05c692-9e98-42f7-a706-11c611fa7dbe · outbound

This paper cites Taylor Shin, Yasaman Razeghi, Robert L.

Training-Free Universal Approximation by Prompting Random Transformers Taylor Shin, Yasaman Razeghi, Robert L

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:14:28.857596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b700c30a-d31b-4787-bb6d-3251da822f4a · outbound

This paper cites an unresolved cited work.

Training-Free Universal Approximation by Prompting Random Transformers Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:14:28.844839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation afaebf6b-be54-4666-9b65-f5e2cae03b2c · outbound

This paper cites Ziqian Zhong and Jacob Andreas.

Training-Free Universal Approximation by Prompting Random Transformers Ziqian Zhong and Jacob Andreas

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T15:14:28.528461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 531442ae-a4dd-4152-bb5c-eae1f8455131 · outbound

This paper cites Aman Bhargava, Cameron Witkowski, Shi-Zhuo Looi, and Matt Thomson.

Training-Free Universal Approximation by Prompting Random Transformers Aman Bhargava, Cameron Witkowski, Shi-Zhuo Looi, and Matt Thomson

Reference 1993

Resolution: unresolved; no resolver link, observed 2026-08-11T15:14:28.444916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef8c7d97-dfa6-4d2d-81de-c4c07362313f · outbound

This paper cites URLhttps://www.sciencedirect.com/ science/article/pii/S1874584901800103.

Training-Free Universal Approximation by Prompting Random Transformers URLhttps://www.sciencedirect.com/ science/article/pii/S1874584901800103

Reference 2001

Resolution: verified exact; doi, observed 2026-08-11T15:14:28.598434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation db644739-6760-4ed6-b99f-c86c427e5eaf · outbound

This paper cites URLhttp://www.jstor.org/stable/20461468.

Training-Free Universal Approximation by Prompting Random Transformers URLhttp://www.jstor.org/stable/20461468

Reference 2007

Resolution: verified exact; raw_fallback, observed 2026-08-11T15:14:28.821419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9167ca07-2d34-4cbb-be02-44bbd6176b36 · outbound

This paper cites doi: 10.1007/s00454-008-9053-2.

Training-Free Universal Approximation by Prompting Random Transformers doi: 10.1007/s00454-008-9053-2

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-11T15:14:28.502151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8944c00-def7-4953-909c-78353ca6bed5 · outbound

This paper cites Kurt Hornik.

Training-Free Universal Approximation by Prompting Random Transformers Kurt Hornik

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-11T15:14:28.468717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc41d120-ad51-4b59-8f49-755ee2832bc6 · outbound

This paper cites doi: https://doi.org/10.1016/j.neunet.2017.07.002.

Training-Free Universal Approximation by Prompting Random Transformers doi: https://doi.org/10.1016/j.neunet.2017.07.002

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-11T15:14:28.523835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0359eeb5-c02f-4b9a-9888-99a2d532ff7b · outbound

This paper cites What's the Magic Word? A Control Theory of LLM Prompting.

Training-Free Universal Approximation by Prompting Random Transformers What's the Magic Word? A Control Theory of LLM Prompting

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T15:14:28.449797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2315d1d7-7bc4-4a57-bebf-441004df4772 · outbound

This paper cites URLhttps://proceedings.neurips.cc/paper_files/paper/2024/file/ 7f64034009f4a5fa417a57e1a987c5cd-Paper-Conference.pdf.

Training-Free Universal Approximation by Prompting Random Transformers URLhttps://proceedings.neurips.cc/paper_files/paper/2024/file/ 7f64034009f4a5fa417a57e1a987c5cd-Paper-Conference.pdf

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T15:14:28.477903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69de66da-b7a2-4dcd-93e9-190b1aee2527 · outbound

This paper cites Is Random Attention Sufficient for Sequence Modeling? Disentangling Trainable Components in the Transformer.

Training-Free Universal Approximation by Prompting Random Transformers Is Random Attention Sufficient for Sequence Modeling? Disentangling Trainable Components in the Transformer

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-11T15:14:28.463703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2c4d46a-e8e6-4268-acc7-4022a34ba82c · outbound

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

Training-Free Universal Approximation by Prompting Random Transformers Understanding In-Context Learning for Nonlinear Regression with Transformers: Attention as Featurizer

Reference 2026

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:14:28.670556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.