Paper Citation Record · LEDGER

Memory Limitations of Prompt Tuning in Transformers

As of 14 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2509.00421.

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

pith.paper-citation-record.v1
2509.00421 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:55:38.945614Z

measured 56 of 56 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:14:28.493489Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T15:14:28.639388Z

Reference resolution

55 of 55 outbound references displayed

verified exact2
verified fuzzy26
unresolved27
parse uncertain0
malformed identifier0
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External citation measurements

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Outbound references

Observation d694635f-e379-4a3e-a5d0-f0a115d0e51c · outbound

This paper cites an unresolved cited work.

Memory Limitations of Prompt Tuning in Transformers Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:55:43.638901Z

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Observation c9c21f19-3f45-4bb5-9b55-539d37eec7cd · outbound

This paper cites Language models are few-shot learners.

Memory Limitations of Prompt Tuning in Transformers Language models are few-shot learners

Reference 2

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Source-reported events for the cited work

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Observation 1aff418a-db5e-494e-8d9b-aa07aad35ff0 · outbound

This paper cites How Smooth Is Attention?.

Memory Limitations of Prompt Tuning in Transformers How Smooth Is Attention?

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:34.581643Z

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Observation e04ea093-c72a-49de-9d2f-d496790744c3 · outbound

This paper cites PLOT : Prompt learning with optimal transport for vision-language models.

Memory Limitations of Prompt Tuning in Transformers PLOT : Prompt learning with optimal transport for vision-language models

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:43.607342Z

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Observation 85bb2937-6ba1-4524-bf49-fdd51f77fc3d · outbound

This paper cites an unresolved cited work.

Memory Limitations of Prompt Tuning in Transformers Unresolved cited work

Reference 5

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Observation 53e2379e-cc00-4e97-83b4-47867ad125b4 · outbound

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

Memory Limitations of Prompt Tuning in Transformers BERT : Pre-training of deep bidirectional transformers for language understanding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:34.841926Z

Source-reported events for the cited work

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Observation 53fd1b81-786c-44e8-ad7e-fc5e48784fc5 · outbound

This paper cites Longrope: extending llm context window beyond 2 million tokens.

Memory Limitations of Prompt Tuning in Transformers Longrope: extending llm context window beyond 2 million tokens

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:43.563057Z

Source-reported events for the cited work

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Observation 78280e05-46b1-4986-9bab-1e6f3bf48fb3 · outbound

This paper cites A survey on in-context learning.

Memory Limitations of Prompt Tuning in Transformers A survey on in-context learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:35.008780Z

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Observation b736311c-4d12-413b-8db8-e802608283cc · outbound

This paper cites Attention is not all you need: Pure attention loses rank doubly exponentially with depth.

Memory Limitations of Prompt Tuning in Transformers Attention is not all you need: Pure attention loses rank doubly exponentially with depth

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:43.539995Z

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

Observation 3d557c22-a8e9-4852-9bdf-695560e61b69 · outbound

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

Memory Limitations of Prompt Tuning in Transformers An image is worth 16x16 words: Transformers for image recognition at scale

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:35.173857Z

Source-reported events for the cited work

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Observation bb276da0-81c1-4789-8e6c-9389e573bff8 · outbound

This paper cites Nemesis: Normalizing the soft-prompt vectors of vision-language models.

Memory Limitations of Prompt Tuning in Transformers Nemesis: Normalizing the soft-prompt vectors of vision-language models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:43.501956Z

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 ff051185-7847-4830-b4ba-f7c8002a9ed6 · outbound

This paper cites Protein multimer structure prediction via prompt learning.

Memory Limitations of Prompt Tuning in Transformers Protein multimer structure prediction via prompt learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:43.482920Z

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 f896dea3-0b21-4283-8953-23b9b74d6bed · outbound

This paper cites The emergence of clusters in self-attention dynamics.

Memory Limitations of Prompt Tuning in Transformers The emergence of clusters in self-attention dynamics

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:43.457179Z

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 71c44911-fbc4-44e5-b2d5-903beb01fc65 · outbound

This paper cites Universal language model fine-tuning for text classification.

Memory Limitations of Prompt Tuning in Transformers Universal language model fine-tuning for text classification

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:35.485603Z

Source-reported events for the cited work

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Observation 81025f6f-86ce-4e26-9d09-b3155978e6f8 · outbound

This paper cites RULER : What s the real context size of your long-context language models? In First Conference on Language Modeling, 2024.

Memory Limitations of Prompt Tuning in Transformers RULER : What s the real context size of your long-context language models? In First Conference on Language Modeling, 2024

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:35.576937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90b3c586-9da8-4093-81b3-c9df080aea44 · outbound

This paper cites Fundamental limits of prompt tuning transformers: Universality, capacity and efficiency.

Memory Limitations of Prompt Tuning in Transformers Fundamental limits of prompt tuning transformers: Universality, capacity and efficiency

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:43.409272Z

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

Observation 268a0c3a-8fa1-4b98-a9c1-8e9a4e8927e2 · outbound

This paper cites Long-context LLM s meet RAG : Overcoming challenges for long inputs in RAG.

Memory Limitations of Prompt Tuning in Transformers Long-context LLM s meet RAG : Overcoming challenges for long inputs in RAG

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:43.311848Z

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 3c863708-8065-4626-83e4-c4f23d179139 · outbound

This paper cites Are transformers with one layer self-attention using low-rank weight matrices universal approximators? In The Twelfth International Conference on Learning Representations, 2024.

Memory Limitations of Prompt Tuning in Transformers Are transformers with one layer self-attention using low-rank weight matrices universal approximators? In The Twelfth International Conference on Learning Representations, 2024

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:43.206793Z

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 8db88d5e-7b9b-42f2-8276-0fdd5514c843 · outbound

This paper cites On the optimal memorization capacity of transformers.

Memory Limitations of Prompt Tuning in Transformers On the optimal memorization capacity of transformers

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:43.045649Z

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 ab847f5e-fac6-4c96-9b69-54011b05c723 · outbound

This paper cites Maple: Multi-modal prompt learning.

Memory Limitations of Prompt Tuning in Transformers Maple: Multi-modal prompt learning

Reference 20

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Source-reported events for the cited work

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Observation 4dc8cf60-7445-4c6b-acca-d7e08ead0dac · outbound

This paper cites The lipschitz constant of self-attention.

Memory Limitations of Prompt Tuning in Transformers The lipschitz constant of self-attention

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:42.789501Z

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 9aeda08e-6145-4df2-bed9-ac77fe37283c · outbound

This paper cites Provable memorization capacity of transformers.

Memory Limitations of Prompt Tuning in Transformers Provable memorization capacity of transformers

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:42.628313Z

Source-reported events for the cited work

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Observation ba1fefdf-42a9-4e32-bac1-0ef4aa122222 · outbound

This paper cites A generalization of hausdorff dimension applied to hilbert cubes and wasserstein spaces.

Memory Limitations of Prompt Tuning in Transformers A generalization of hausdorff dimension applied to hilbert cubes and wasserstein spaces

Reference 23

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Source-reported events for the cited work

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Observation ca74eb65-47bd-44d8-a216-1faa4a277614 · outbound

This paper cites Kloeckner.

Memory Limitations of Prompt Tuning in Transformers Kloeckner

Reference 24

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Source-reported events for the cited work

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Observation 70f6faba-3f95-42a6-baae-f2420adeb2e6 · outbound

This paper cites Attention is not only a weight: Analyzing transformers with vector norms.

Memory Limitations of Prompt Tuning in Transformers Attention is not only a weight: Analyzing transformers with vector norms

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:36.387037Z

Source-reported events for the cited work

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Observation 01de0587-fedd-4116-8f31-1d724929282b · outbound

This paper cites Large language models are zero-shot reasoners.

Memory Limitations of Prompt Tuning in Transformers Large language models are zero-shot reasoners

Reference 26

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Source-reported events for the cited work

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Observation 7fadf5e8-ddfb-444b-b2a7-985f82437731 · outbound

This paper cites Summary of a haystack: A challenge to long-context LLM s and RAG systems.

Memory Limitations of Prompt Tuning in Transformers Summary of a haystack: A challenge to long-context LLM s and RAG systems

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:36.551798Z

Source-reported events for the cited work

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Observation c42dbf91-1aa1-4198-9866-d971d4950483 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

Memory Limitations of Prompt Tuning in Transformers The power of scale for parameter-efficient prompt tuning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:36.639752Z

Source-reported events for the cited work

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Observation c232052a-e76f-4821-bfd3-813004205b28 · outbound

This paper cites Same task, more tokens: the impact of input length on the reasoning performance of large language models.

Memory Limitations of Prompt Tuning in Transformers Same task, more tokens: the impact of input length on the reasoning performance of large language models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:36.764183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d07376a-23c1-4fa0-8a63-414d7bd59bf6 · outbound

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Memory Limitations of Prompt Tuning in Transformers Unresolved cited work

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:36.835953Z

Source-reported events for the cited work

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Observation 9ec11661-2a1d-4b2a-8d9e-293197a2d4a3 · outbound

This paper cites Extending context window in large language models with segmented base adjustment for rotary position embeddings.

Memory Limitations of Prompt Tuning in Transformers Extending context window in large language models with segmented base adjustment for rotary position embeddings

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:42.362979Z

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 f48d54ef-0e87-41c0-bd99-e7d9c38b668b · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

Memory Limitations of Prompt Tuning in Transformers Prefix-tuning: Optimizing continuous prompts for generation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:36.969710Z

Source-reported events for the cited work

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Observation 81aa6a6d-de15-4485-850c-ab674ade216a · outbound

This paper cites Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang.

Memory Limitations of Prompt Tuning in Transformers Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:37.046757Z

Source-reported events for the cited work

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Observation 590ec142-424a-462b-8b48-a8c38f5ec460 · outbound

This paper cites Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer.

Memory Limitations of Prompt Tuning in Transformers Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:42.102232Z

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

Observation 5ee23938-4888-4b05-a087-2fb716bc79c5 · outbound

This paper cites P -tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks.

Memory Limitations of Prompt Tuning in Transformers P -tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:37.213765Z

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Observation d532d426-13b3-4fe5-97dd-0e6e41914ae9 · outbound

This paper cites Memorization capacity of multi-head attention in transformers.

Memory Limitations of Prompt Tuning in Transformers Memorization capacity of multi-head attention in transformers

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:41.771357Z

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 42d6cf5f-f323-4985-91b5-c94a54103421 · outbound

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

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

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

Observation d3309bd7-8589-4bac-9878-b61b3e916ee9 · outbound

This paper cites Convergence of latent mixing measures in finite and infinite mixture models.

Memory Limitations of Prompt Tuning in Transformers Convergence of latent mixing measures in finite and infinite mixture models

Reference 38

Resolution: verified exact; raw_fallback, observed 2026-08-05T13:55:39.420969Z

Source-reported events for the cited work

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Observation 1743bff9-50dc-462c-8d04-641f8fe5bb3a · outbound

This paper cites GPT-4 Technical Report.

Memory Limitations of Prompt Tuning in Transformers GPT-4 Technical Report

Reference 39

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Source-reported events for the cited work

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Observation 7c20c6ae-5f4b-4246-8314-aaeeaad3964c · outbound

This paper cites On the role of attention in prompt-tuning.

Memory Limitations of Prompt Tuning in Transformers On the role of attention in prompt-tuning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:41.507163Z

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 d0173a22-322a-401c-b861-4a26d28837af · outbound

This paper cites Prompting a pretrained transformer can be a universal approximator.

Memory Limitations of Prompt Tuning in Transformers Prompting a pretrained transformer can be a universal approximator

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:41.120643Z

Source-reported events for the cited work

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Observation 70460bc2-92a1-4387-b2e8-4397c38a078d · outbound

This paper cites When do prompting and prefix-tuning work? a theory of capabilities and limitations.

Memory Limitations of Prompt Tuning in Transformers When do prompting and prefix-tuning work? a theory of capabilities and limitations

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:37.777322Z

Source-reported events for the cited work

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Observation a225bc07-db4b-459a-ac38-cdf7d78585c5 · outbound

This paper cites Sander, Pierre Ablin, Mathieu Blondel, and Gabriel Peyr\'e.

Memory Limitations of Prompt Tuning in Transformers Sander, Pierre Ablin, Mathieu Blondel, and Gabriel Peyr\'e

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:40.967306Z

Source-reported events for the cited work

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Observation eb5d1de3-61e0-4522-a0ad-033618995047 · outbound

This paper cites Optimal transport for applied mathematicians.

Memory Limitations of Prompt Tuning in Transformers Optimal transport for applied mathematicians

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:40.800918Z

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 65c8f1d0-559a-4140-85b1-2f8d690fd3db · outbound

This paper cites De PT : Decomposed prompt tuning for parameter-efficient fine-tuning.

Memory Limitations of Prompt Tuning in Transformers De PT : Decomposed prompt tuning for parameter-efficient fine-tuning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:38.052182Z

Source-reported events for the cited work

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Observation 4d351f30-e34d-4537-a73a-9b27a7303ac2 · outbound

This paper cites Attention is all you need.

Memory Limitations of Prompt Tuning in Transformers Attention is all you need

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:38.145382Z

Source-reported events for the cited work

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Observation b7ef253a-77fe-4877-bec2-7d1e52f1d371 · outbound

This paper cites High-Dimensional Probability: An Introduction with Applications in Data Science.

Memory Limitations of Prompt Tuning in Transformers High-Dimensional Probability: An Introduction with Applications in Data Science

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:40.642760Z

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 8a0a8b38-b50c-4e99-8e91-be023be525f8 · outbound

This paper cites Optimal Transport: Old and New, volume 338 of Grundlehren der mathematischen Wissenschaften.

Memory Limitations of Prompt Tuning in Transformers Optimal Transport: Old and New, volume 338 of Grundlehren der mathematischen Wissenschaften

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:40.540466Z

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 1933e6ce-934d-461f-bd04-ff59eee02673 · outbound

This paper cites Universality and limitations of prompt tuning.

Memory Limitations of Prompt Tuning in Transformers Universality and limitations of prompt tuning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:40.365417Z

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 dbbe1ad9-efc0-4a55-879e-4eebc2ae881a · outbound

This paper cites Multitask prompt tuning enables parameter-efficient transfer learning.

Memory Limitations of Prompt Tuning in Transformers Multitask prompt tuning enables parameter-efficient transfer learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:40.237484Z

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 293b85d7-7d86-4cb1-a897-c1ee4f69217e · outbound

This paper cites Chi, Quoc V.

Memory Limitations of Prompt Tuning in Transformers Chi, Quoc V

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:38.559865Z

Source-reported events for the cited work

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Observation 3f5920eb-5cd2-4ca3-8f67-2c93964a77f3 · outbound

This paper cites Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery.

Memory Limitations of Prompt Tuning in Transformers Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:40.101391Z

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 7684d930-d7e7-4eff-8745-f991de223cea · outbound

This paper cites Transformers: State-of-the-art natural language processing.

Memory Limitations of Prompt Tuning in Transformers Transformers: State-of-the-art natural language processing

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-05T13:55:38.794844Z

Source-reported events for the cited work

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Observation 08f3f379-9b75-4315-82c8-e83d8ccf31e0 · outbound

This paper cites Scaling vision transformers.

Memory Limitations of Prompt Tuning in Transformers Scaling vision transformers

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:39.958456Z

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 05c3ed40-678a-4267-8394-d4ab44d5a479 · outbound

This paper cites Point transformer.

Memory Limitations of Prompt Tuning in Transformers Point transformer

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:55:39.831036Z

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Pith citing papers

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

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

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

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