Paper Citation Record · LEDGER

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs

As of 10 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2506.07587.

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

pith.paper-citation-record.v1
2506.07587 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:35:20.206546Z

measured 21 of 21 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:51:18.466788Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T23:50:52.675006Z

Reference resolution

20 of 20 outbound references displayed

verified exact1
verified fuzzy3
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9114de08-f057-4f7e-b8c8-c69548095bee · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.143648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd17ceb5-dfe2-49d4-866c-1845ea576a5d · outbound

This paper cites AdapterFusion: Non-Destructive Task Composition for Transfer Learning.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.151362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 893469f6-6f61-445a-b117-77ab73870ae6 · outbound

This paper cites Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.155261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a362526e-a552-4b43-975d-7867b651620c · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.159227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79b4c205-0cc0-43f2-a3bb-f55588cd6450 · outbound

This paper cites MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:35:20.333309Z

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.

Observation da7fc3c7-e972-4460-a171-a32463387dcb · outbound

This paper cites AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.170246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 315b20f0-4cc1-472b-b85b-dfceb9c29e36 · outbound

This paper cites Dora: Weight-decomposed low-rank adaptation.ICML 2024,.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs Dora: Weight-decomposed low-rank adaptation.ICML 2024,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.467487Z

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.

Observation 99d0d9cd-d21c-46c6-ae39-3b72deb90d6f · outbound

This paper cites On the Effect of Dropping Layers of Pre-trained Transformer Models.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs On the Effect of Dropping Layers of Pre-trained Transformer Models

Reference 13

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:35:20.298704Z

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.

Observation 4c3b3d44-8668-4304-bc2c-384561bc8660 · outbound

This paper cites Probing pretrained language models for lexical semantics.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs Probing pretrained language models for lexical semantics

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.456586Z

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.

Observation d594b4ae-c5e1-42c0-8dd7-f5156b0b6694 · outbound

This paper cites Parameter-Efficient Fine-Tuning Design Spaces.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs Parameter-Efficient Fine-Tuning Design Spaces

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.195445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e26d6800-9d60-4262-b3b4-ef343fb65ceb · outbound

This paper cites Datasets: A Community Library for Natural Language Processing.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs Datasets: A Community Library for Natural Language Processing

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.199005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7605fc6d-826f-4505-b753-4adb3b0bb590 · outbound

This paper cites AdapterHub: A Framework for Adapting Transformers.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs AdapterHub: A Framework for Adapting Transformers

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.202596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60630552-b47d-43ed-a219-dc3aee264f25 · outbound

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

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.206546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4b00561-cae5-4e44-a113-a287d818e1f3 · outbound

This paper cites Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.184803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18103730-bff9-407b-b741-fadd7058c342 · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.177613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7b57d7e-27d3-47dd-a5e6-8a3c759f9b58 · outbound

This paper cites Auto-keras: An efficient neural architecture search system.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs Auto-keras: An efficient neural architecture search system

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.477914Z

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.

Observation 94577200-2b90-490b-a9b3-8bd5d796387e · outbound

This paper cites BERT Rediscovers the Classical NLP Pipeline.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs BERT Rediscovers the Classical NLP Pipeline

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.191800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 153a104c-87aa-4d72-8785-3835d1aa3e91 · outbound

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

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs LoRA: Low-Rank Adaptation of Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.139893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3ff001d-7415-4c3c-a478-95bd87511714 · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models.arXiv preprint arXiv:2106.10199,.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models.arXiv preprint arXiv:2106.10199,

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.135969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdf6c6db-7c5b-48b9-a443-307acc0c4f4e · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.147596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 8ece6cc0-cd04-46b6-93d0-d8cf91c4f667 · inbound

Constraint-Driven Warm-Freeze for Efficient Transfer Learning in Photovoltaic Systems cites this paper.

Constraint-Driven Warm-Freeze for Efficient Transfer Learning in Photovoltaic Systems PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:50:52.707893Z

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.