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Paper Citation Record · LEDGER

A Theoretical Framework for Prompt Engineering: Approximating Smooth Functions with Transformer Prompts

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.20561.

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

pith.paper-citation-record.v1
2503.20561 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:14:28.497840Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T13:55:39.506701Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 42d6cf5f-f323-4985-91b5-c94a54103421 · inbound

Memory Limitations of Prompt Tuning in Transformers cites this paper.

Memory Limitations of Prompt Tuning in Transformers A Theoretical Framework for Prompt Engineering: Approximating Smooth Functions with Transformer Prompts

Reference 37

Resolution
verified exact
local_arxiv, observed 2026-08-05T13:55:39.558086Z

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.

source=arxiv_source observed=2026-08-05T13:55:37.382618Z digest=sha256:4778962422e74ff29899a310dad396c49a61a2a4c5bba2d090282cb813327013

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

Training-Free Universal Approximation by Prompting Random Transformers cites this paper.

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.

source=pdf_text observed=2026-08-11T15:14:28.497840Z digest=sha256:fe8d486fedc213bdd615395ab2f5f89adfe5817b110471e14a28705125b80018