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Paper Citation Record · LEDGER

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs

As of 10 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2506.16196.

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

pith.paper-citation-record.v1
2506.16196 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:51:22.104083Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

38 of 38 outbound references displayed

  • verified exact3
  • verified fuzzy14
  • unresolved20
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 76228f22-a172-4a48-bcda-a7a03ed80027 · outbound

This paper cites B., Mironov, I., Talwar, K., and Zhang, L.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs B., Mironov, I., Talwar, K., and Zhang, L

Reference 1

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:51:19.116953Z digest=sha256:d72ec3e9fffdef99beca33e360de78dc5e8c5ebc43b707089cf5e7f3d7f0ef75

Observation 8047108a-9082-45ec-9310-f39634172f65 · outbound

This paper cites Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K

Reference 5

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:51:19.512115Z digest=sha256:864d0631129e7c1a84f308382706d438affcfcc0c79f6fa10eb23e7cb8fb46f1

Observation d80fe9a4-796f-48d3-9405-f1fc0dd9edc2 · outbound

This paper cites Cal- ibrating noise to sensitivity in private data analysis.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Cal- ibrating noise to sensitivity in private data analysis

Reference 7

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source=pdf_text observed=2026-08-06T23:51:19.671263Z digest=sha256:4bb4cdeef12103403d1d72ad9c9faf535f3f7efd6b24abd9534dcc7eb13f42f0

Observation 797b0a7c-009d-4578-822e-cf5dedc79aa8 · outbound

This paper cites αce αlm αcos Learning Rate Batch Size 5.0 2.0 1.0 0.00025 5 B.2.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs αce αlm αcos Learning Rate Batch Size 5.0 2.0 1.0 0.00025 5 B.2

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:51:21.791638Z digest=sha256:b810ec4c3d32f7f4d5372552cc804f60cca61dba4543f4011be1e7b8b56e5093

Observation b4596e6e-b17c-4474-896e-3f98ff5a7438 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Distilling the Knowledge in a Neural Network

Reference 11

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source=pdf_text observed=2026-08-06T23:51:20.050671Z digest=sha256:493f1af214b087272387fad038022f9c417701227542e4831890006e6e7bf96d

Observation 6a9eca4c-6b78-4c6e-95fe-46492d23e2c6 · outbound

This paper cites an unresolved cited work.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Unresolved cited work

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:51:21.864863Z digest=sha256:70d5d3d5798760f250e9af16ca7c4775e45f51a1eb70afa52b3ce7e45f06742a

Observation ed35300b-a9a1-4ed3-bfef-242a28a64d55 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 14

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source=pdf_text observed=2026-08-06T23:51:20.287639Z digest=sha256:1d7f5a8528bbe77ae73854135f0d57afe7aa9e9b30a44120dae6672044cb8eb5

Observation 37cdd5d4-ecd6-4e52-bd08-210b97a12923 · outbound

This paper cites an unresolved cited work.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Unresolved cited work

Reference 16

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source=pdf_text observed=2026-08-06T23:51:20.428659Z digest=sha256:dc35b4df27227c631c0de6bb9c5c7c29f7d0b2ef9befaabca7573a55c7251779

Observation 65163342-def3-40e8-9743-2da96ab12d82 · outbound

This paper cites A conversational movie search system based on con- ditional random fields.13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012, 3, 01.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs A conversational movie search system based on con- ditional random fields.13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012, 3, 01

Reference 17

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source=pdf_text observed=2026-08-06T23:51:20.508680Z digest=sha256:92882da20b03a31133bc783c9aff83e64b4c44f2c1832591de9637fb09f33b3e

Observation d362a222-b989-4f37-b654-6cac82bca36c · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 19

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source=pdf_text observed=2026-08-06T23:51:20.635636Z digest=sha256:7056dc193e13772e3da6d4b99140c6617045a3af1212bca9a3b07ab4e26c98f0

Observation dd3fc778-2770-4e63-af55-ea5981a0cb71 · outbound

This paper cites Bissyand ´e, T.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Bissyand ´e, T

Reference 20

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source=pdf_text observed=2026-08-06T23:51:20.707568Z digest=sha256:e7840d68e77110fe97dc6839ece514e607ca9530e35456664bc9d7822c1ffd45

Observation e3594ea4-0a6c-4f55-97f8-6376b732f6ed · outbound

This paper cites URL https://aclanthology.org/ 2024.moomin-1.2/.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs URL https://aclanthology.org/ 2024.moomin-1.2/

Reference 21

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:51:20.781502Z digest=sha256:d13440a9ef2e38cfff933eeccba85a5cb566eacdd0d70b7cc17f9b268b00d707

Observation d25ac3da-0e65-42d0-9ff6-6e56da1f5dbc · outbound

This paper cites an unresolved cited work.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Unresolved cited work

Reference 22

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:51:20.870513Z digest=sha256:8b5eafaccdaf58e14a9842ffd81441e418f1fe577694450986f61fe10402bf98

Observation 5870faa5-9aac-4653-b85b-49f3a0328f47 · outbound

This paper cites Semeval-2017 task 4: Sentiment analysis in twitter.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Semeval-2017 task 4: Sentiment analysis in twitter

Reference 23

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:51:20.940534Z digest=sha256:a2c9648d484eb5e07f32fe2d542e7cd6b688a0b2d8717b2a540103ebd0c09f10

Observation 99db9a13-2fee-4f4e-9daf-28672702bcf0 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 24

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source=pdf_text observed=2026-08-06T23:51:21.021456Z digest=sha256:ab1f34281521424c382aee9b70e7e490268f1a8a3065963c65de223b53fab816

Observation 5119602e-b12a-4956-8969-5e331bcd3e6a · outbound

This paper cites Few-Shot Text Generation with Pattern-Exploiting Training.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Few-Shot Text Generation with Pattern-Exploiting Training

Reference 25

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local_arxiv, observed 2026-08-06T23:51:22.925163Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:51:21.108437Z digest=sha256:dfc795686b8c092398f97aa1113817ae9e0c166ef4c7c744029818a3f40e602d

Observation 048e2eda-9b1a-4bc5-81c1-531fd964d778 · outbound

This paper cites L., Wallace, E., and Singh, S.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs L., Wallace, E., and Singh, S

Reference 27

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:51:21.271251Z digest=sha256:2c16a6f94e1b370f0fc499984a7fb6b8f753f62ab5f5f90cb4f183606b975fd1

Observation cff09b21-b52d-4ed2-81e9-948ee10bea16 · outbound

This paper cites LLM Pruning and Distillation in Practice: The Minitron Approach.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs LLM Pruning and Distillation in Practice: The Minitron Approach

Reference 28

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source=pdf_text observed=2026-08-06T23:51:21.346388Z digest=sha256:b3a51d15fad2204c6b5033feb6c528a9ee14eed005a8edb1b321faf8a2e3c822

Observation 5b7ce82a-9e8d-4e0d-9b42-4dd652414769 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 29

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source=pdf_text observed=2026-08-06T23:51:21.432610Z digest=sha256:35a48e96e85c2b50985013919d569dbcd426741e6adcd5bc6eea6eba751fd076

Observation 419ae4c0-cba7-4205-be56-549852550208 · outbound

This paper cites Structurally Prune Anything: Any Architecture, Any Framework, Any Time.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Structurally Prune Anything: Any Architecture, Any Framework, Any Time

Reference 30

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source=pdf_text observed=2026-08-06T23:51:21.522905Z digest=sha256:8e08ddd2e997f4aff03885f33efa7a12a71d4d8bd556d644d63b844778f60d23

Observation 7261a1c6-85f6-413d-b75e-b6e44e88514d · outbound

This paper cites Offsite-Tuning: Transfer Learning without Full Model.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Offsite-Tuning: Transfer Learning without Full Model

Reference 32

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source=pdf_text observed=2026-08-06T23:51:21.660873Z digest=sha256:cba8b508d0b024c2f7a41d66ea76c23ac217203350282448a86d62ae463d6887

Observation f712776f-e0d3-4799-b1ff-7d4e8cad9fc8 · outbound

This paper cites Factual probing is [mask]: Learning vs.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Factual probing is [mask]: Learning vs

Reference 33

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source=pdf_text observed=2026-08-06T23:51:21.715226Z digest=sha256:231fc07c56598071246e2a4827c9293791f8c459683ba2fd65f5484c354d0ccd

Observation a4ef57da-cba5-4692-ac83-305d21726c2e · outbound

This paper cites The results show that using the same dataset for both private and public sets consistently achieves the best performance.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs The results show that using the same dataset for both private and public sets consistently achieves the best performance

Reference 36

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:51:21.928244Z digest=sha256:32bebf46eb7320162428b0f00f7cc83874e58a5d0bb6f9455326b7e516f0ff89

Observation 90bdd5dc-8b91-41b0-8d07-e3fff49bdb9b · outbound

This paper cites an unresolved cited work.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Unresolved cited work

Reference 37

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source=pdf_text observed=2026-08-06T23:51:21.999257Z digest=sha256:d55cd59f6122d6c6a9379edc11e16cee84797b49bae7c7879fb2a3834cd6de2d

Observation 070c9392-c974-4068-ab63-2b254027bbe1 · outbound

This paper cites Knowledge Distillation Setup.We also investigated the best way of performing KD to improve prompt transferability.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Knowledge Distillation Setup.We also investigated the best way of performing KD to improve prompt transferability

Reference 38

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source=pdf_text observed=2026-08-06T23:51:22.040979Z digest=sha256:9ab0f69ef49e3072304b2ecfec07aedbce096f8bded47f009c305624a975a915

Observation f9884a3c-e3fe-4736-af3c-b05612f32a16 · outbound

This paper cites Both too few or too many tokens in the soft prompt lead to sub-optimal performance.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Both too few or too many tokens in the soft prompt lead to sub-optimal performance

Reference 39

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source=pdf_text observed=2026-08-06T23:51:22.104083Z digest=sha256:ef7ebcab74a2a1a38b419907cff57cc5289f433da8444904537a6ec2cfedf641

Observation 94b7ddcc-24fc-4c06-bfca-fa87003e2793 · outbound

This paper cites Membership inference attacks from first principles.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Membership inference attacks from first principles

Reference 2006

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:51:19.323002Z digest=sha256:095cb05b61aa6d7397af46617002559e49c6ec04324207db2f621f01c3c6eb1c

Observation a1c1a4b2-a234-4d46-a4c5-d8cceae247e9 · outbound

This paper cites DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer

Reference 2015

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source=pdf_text observed=2026-08-06T23:51:20.136653Z digest=sha256:a83a89583166557b9968a98770016f2994974a20c95362391f335886b229ec09

Observation 2da8cb45-101c-47f5-9b7f-de67a87f7f38 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 2016

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source=pdf_text observed=2026-08-06T23:51:19.949589Z digest=sha256:a5748ff674eeadf86468a91c7478f2cf67af963bdf6f30807859fcc5d48e39ea

Observation bd9aa433-6047-4d8c-8979-32fdc8741619 · outbound

This paper cites an unresolved cited work.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Unresolved cited work

Reference 2017

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source=pdf_text observed=2026-08-06T23:51:20.366245Z digest=sha256:190b9759e1c6b25c09245dcacbdbb17f0ac4e9bffc407205f02d646440ec7980

Observation 423c16e3-a107-4dbf-b123-0926c6c87fe3 · outbound

This paper cites Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Reference 2018

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source=pdf_text observed=2026-08-06T23:51:20.222981Z digest=sha256:20b4e151767e8da2d99e96001ba39a78f44a9155c8886b6727fed598858a0143

Observation 8a789b62-2649-4c29-9b6e-a1c007fd3242 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2019

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source=pdf_text observed=2026-08-06T23:51:19.593363Z digest=sha256:616f4628aa59757d45db88f01c78fdf1b8ca84299a35d4b1f1b417c56b128ab8

Observation 0b211430-49fb-42c2-a747-476ffd90ded6 · outbound

This paper cites and Sch¨utze, H.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs and Sch¨utze, H

Reference 2020

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raw_fallback, observed 2026-08-06T23:51:24.216230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:51:21.195059Z digest=sha256:441e10203580322f844d2c287baa9a78e83a8a3932260381b0ea745ebf71e43c

Observation 4a4d1bfe-a61c-4afb-b5bb-f399c6a81271 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Gemini: A Family of Highly Capable Multimodal Models

Reference 2021

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source=pdf_text observed=2026-08-06T23:51:19.739874Z digest=sha256:f51b32d7477a490be7cde0816f0fcfcafa6b3ad8bfe83cdf7101790c65338f17

Observation 696aacdb-eef5-4de3-97b7-26b3362e05a1 · outbound

This paper cites Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y ., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y ., Gonzalez, J.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y ., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y ., Gonzalez, J

Reference 2022

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:51:19.397146Z digest=sha256:49e87bdf150418baa2dacec99cb94d665cef672a2bd289fac216c071934acb8b

Observation b0748cc4-9869-4bdf-bc2a-c106256673d2 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-06T23:51:19.213684Z

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source=pdf_text observed=2026-08-06T23:51:19.213684Z digest=sha256:79e289f98a9fa720f97a99d338ab7cad7c6193eeb8011d8144b58f42f3d5f297

Observation ba6051aa-b2c9-497f-8b36-ff74c6ed5b70 · outbound

This paper cites Hambardzumyan, K., Khachatrian, H., and May, J.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Hambardzumyan, K., Khachatrian, H., and May, J

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:51:25.854993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:51:19.860167Z digest=sha256:7e5c06bd93a445058122e1e8a1391650ffa6bf08fdd626a8a1e0d306ef8e0318

Observation c899c6ad-5ee1-4016-b8f2-01d072ed3f1f · outbound

This paper cites Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-06T23:51:21.598507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:51:21.598507Z digest=sha256:53c79c95e0f69837a3a854689c17039fa8ab742fb228feeddfa66c26319eb734

Pith citing papers

No inbound Pith citation observations are available.