Paper Citation Record · LEDGER

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models

As of 19 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2504.20570.

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

pith.paper-citation-record.v1
2504.20570 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:31:27.452275Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T01:39:19.183198Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T18:45:58.992084Z

Reference resolution

45 of 45 outbound references displayed

verified exact0
verified fuzzy12
unresolved33
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5ac832ea-21af-4a70-85a6-8d7721f9e4a2 · outbound

This paper cites GPT-4 Technical Report.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.243720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c7964ef-7421-4708-b2e4-30215180974d · outbound

This paper cites Attention is all you need,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Attention is all you need,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.249363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 779f8801-81f8-4cc9-baef-35c55d33dc4a · outbound

This paper cites A Survey of Large Language Models.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models A Survey of Large Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.254245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 898168e2-98b7-4c05-9189-f45e160b2cab · outbound

This paper cites Scaling Laws for Neural Language Models.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Scaling Laws for Neural Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.259573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06fc9b4d-6600-4fea-902f-8ee42f07ca78 · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.264640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39ecf5e8-9d91-45d8-b726-15b7690a0bf8 · outbound

This paper cites Parameter-efficient fine-tuning of large- scale pre-trained language models,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Parameter-efficient fine-tuning of large- scale pre-trained language models,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.269335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44ad381a-bbf7-4fc6-9f95-c98be9b1711d · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.274665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0eab4788-4812-40f9-9e24-d97d55280890 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.279505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b12c2171-8902-4417-8077-a83e47f51525 · outbound

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

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Offsite-Tuning: Transfer Learning without Full Model

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.284044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4bd2977-1609-42bf-914b-1e9c7a8427dd · outbound

This paper cites Fedlegal: The first real-world federated learning benchmark for legal nlp,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Fedlegal: The first real-world federated learning benchmark for legal nlp,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:31:28.207407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 761fdabe-59f9-4a60-973b-e9d5bdf1e801 · outbound

This paper cites Improving LoRA in Privacy-preserving Federated Learning.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Improving LoRA in Privacy-preserving Federated Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.293628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62ae2dcd-9b55-44e2-8b90-c730489b37cd · outbound

This paper cites Deep leakage from gradients,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Deep leakage from gradients,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.299066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09bce3d0-7db8-40e0-9456-bd5eb420c7af · outbound

This paper cites Lamp: Extracting text from gradients with language model priors,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Lamp: Extracting text from gradients with language model priors,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.303694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0988facd-9c86-494a-8cf4-9f7197967ab5 · outbound

This paper cites DAGER: Exact Gradient Inversion for Large Language Models.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models DAGER: Exact Gradient Inversion for Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.308484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c910254-7e02-42bc-81c2-bd93dc6326d4 · outbound

This paper cites Quantifying Memorization Across Neural Language Models.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Quantifying Memorization Across Neural Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.313285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01508bbe-f781-4785-b3d3-cda9607f59e0 · outbound

This paper cites Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.318059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2af6dd09-c9ff-4f31-b42e-686036d5a472 · outbound

This paper cites Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.322737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8990b79c-7c0f-499f-b542-32f47c6eff48 · outbound

This paper cites Teach LLMs to Phish: Stealing Private Information from Language Models.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Teach LLMs to Phish: Stealing Private Information from Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.327680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db54f403-5f5b-4dfd-bf5e-28f6b71f21ad · outbound

This paper cites Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.332892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ef76844-d0b4-4744-a924-fde326d3d750 · outbound

This paper cites Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.337356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 108ecdc3-8d83-45a4-8ab5-f7c6f1425463 · outbound

This paper cites Loki: Large-scale data reconstruction attack against fed- erated learning through model manipulation,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Loki: Large-scale data reconstruction attack against fed- erated learning through model manipulation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:31:28.173916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f47771f0-b41f-4cf3-957b-cdabb5db648f · outbound

This paper cites Exploring Memorization in Fine-tuned Language Models.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Exploring Memorization in Fine-tuned Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.346627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e935697-05e9-42a2-bdaf-a69d17b1129f · outbound

This paper cites Be like a goldfish, don’t memorize! mitigating memorization in generative llms,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Be like a goldfish, don’t memorize! mitigating memorization in generative llms,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:31:28.158765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0345d142-61c7-4eba-bc3d-2cc51535c0ec · outbound

This paper cites Analyzing leakage of personally identifiable information in language models,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Analyzing leakage of personally identifiable information in language models,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:31:28.142679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 62606546-bb03-4bf6-a6e0-f94dc11a4456 · outbound

This paper cites Precurious: How innocent pre- trained language models turn into privacy traps,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Precurious: How innocent pre- trained language models turn into privacy traps,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:31:28.127960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a20198c7-06b2-46d7-9feb-5bdb89282a99 · outbound

This paper cites PII-Compass: Guiding LLM training data extraction prompts towards the target PII via grounding.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models PII-Compass: Guiding LLM training data extraction prompts towards the target PII via grounding

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.364567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83765166-d5a5-4e3a-8c16-f7e1d0b13cb4 · outbound

This paper cites Parameter-efficient transfer learning for nlp,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Parameter-efficient transfer learning for nlp,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.369504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a56aaaf-3277-42e8-b38d-ba2a7ed36052 · outbound

This paper cites Propile: Probing privacy leakage in large language models,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Propile: Probing privacy leakage in large language models,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:31:28.104379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d6622eb1-283a-4406-a934-e92f27f35d35 · outbound

This paper cites Learning to reason and memorize with self-notes,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Learning to reason and memorize with self-notes,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:31:28.090312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c49f98d8-8fff-4979-8eaa-15d094c5fa2b · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.382881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 449028d0-06fb-4078-a7b0-0b5c6006c16e · outbound

This paper cites Cocktail party attack: Breaking aggregation-based privacy in federated learning using independent component analysis,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Cocktail party attack: Breaking aggregation-based privacy in federated learning using independent component analysis,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:31:28.074039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 886c8998-4556-43a2-877e-b20455969fd3 · outbound

This paper cites SPEAR:Exact Gradient Inversion of Batches in Federated Learning.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models SPEAR:Exact Gradient Inversion of Batches in Federated Learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.391858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a053c742-a180-4172-9142-4d45ee39c46f · outbound

This paper cites an unresolved cited work.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:31:28.056266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 30fc6dfc-c649-4c1d-bbb0-9562b3ba797b · outbound

This paper cites Stevenson, Oxford dictionary of English.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Stevenson, Oxford dictionary of English

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:31:28.040613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e9de6b43-c051-4468-93f1-c1a7abb87070 · outbound

This paper cites The enron corpus: A new dataset for email classification research,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models The enron corpus: A new dataset for email classification research,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:31:28.024871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e32bdcc5-5997-456d-9f4e-f91be40365f5 · outbound

This paper cites Personalizing Dialogue Agents: I have a dog, do you have pets too?.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Personalizing Dialogue Agents: I have a dog, do you have pets too?

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.410198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43dcf9c8-fdb2-48d1-adb0-f7aaaf1c512b · outbound

This paper cites Know What You Don't Know: Unanswerable Questions for SQuAD.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Know What You Don't Know: Unanswerable Questions for SQuAD

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.414858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 388acd3c-864b-4e1c-81d6-d5fc03e823a7 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.419325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bdadf13-1f82-419b-8700-f74e46c9ea76 · outbound

This paper cites Crosslingual Generalization through Multitask Finetuning.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Crosslingual Generalization through Multitask Finetuning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.423966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c241f52c-d2f8-468c-93c3-9ca2564228e3 · outbound

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

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.428627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d7d371b-ebed-459a-aa0b-e166d3961b8e · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.433167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a798bb1e-5408-4816-b6ea-92a882c6e551 · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.437908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6899cff-1656-45a8-bff2-0547018ef616 · outbound

This paper cites Uncovering gradient inversion risks in practical language model training,.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Uncovering gradient inversion risks in practical language model training,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:31:28.009848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation eed6d884-d244-44e2-bca2-64fc05a7bc44 · outbound

This paper cites FedAdapter: Efficient Federated Learning for Modern NLP.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models FedAdapter: Efficient Federated Learning for Modern NLP

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T05:31:27.447045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50a731f4-7f53-4f12-b36b-4ed457b36326 · outbound

This paper cites The updated warranty is valid until December 31, 2027.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models The updated warranty is valid until December 31, 2027

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:31:27.994121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation bc853c95-9812-4170-adca-b26077590f1b · inbound

When the Aggregator Cheats: Data-Free Backdoors in Federated LLM-based QA Systems cites this paper.

When the Aggregator Cheats: Data-Free Backdoors in Federated LLM-based QA Systems ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-07-01T18:45:58.994055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.