Paper Citation Record · LEDGER

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models

As of 23 August 2026, this Paper Citation Record lists 100 of 299 outbound references and 4 inbound Pith citation observations for arXiv:2504.14117.

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

pith.paper-citation-record.v1
2504.14117 v1

Coverage vector

measured 100 of 299 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:59:47.934140Z

measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:36:34.168422Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T23:16:36.724034Z

Reference resolution

100 of 299 outbound references displayed

verified exact1
verified fuzzy0
unresolved99
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ab0a0c59-0cc8-4068-a0f5-3ac2b164244c · outbound

This paper cites 6 llm fine-tuning: Instruction and parameter- efficient fine-tuning (peft).

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models 6 llm fine-tuning: Instruction and parameter- efficient fine-tuning (peft)

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.467117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80765bff-9d0f-4a40-9ff4-8ee830c33327 · outbound

This paper cites Text summarization: a brief review.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Text summarization: a brief review

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.483716Z

Source-reported events for the cited work

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Observation 9bcf7320-1215-4b72-be2c-94d1b79f6426 · outbound

This paper cites Text summarization: a brief review.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Text summarization: a brief review

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.488195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78554e57-f720-4751-bb7b-029112c32241 · outbound

This paper cites Transformer models for text-based emotion detection: a review of bert-based approaches.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Transformer models for text-based emotion detection: a review of bert-based approaches

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.492751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4735401-55ce-48d0-8780-a95155c4dfe9 · outbound

This paper cites GPT-4 Technical Report.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models GPT-4 Technical Report

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.497225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e790ae3b-d65f-49de-a182-0ecab2f99d6b · outbound

This paper cites Fine-tuning of algorithms using fractional experimental designs and local search.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Fine-tuning of algorithms using fractional experimental designs and local search

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.502134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 973b1f21-2c48-4894-bc1e-2448e4656b5a · outbound

This paper cites Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.507280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f29d3d91-184d-4070-b949-a6f9f01f3ae6 · outbound

This paper cites Efficient interactive decision- making framework for robotic applications.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Efficient interactive decision- making framework for robotic applications

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.511399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5700eae-10e3-4d4b-b6e5-1a515fa3eb22 · outbound

This paper cites Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.514866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e699258a-e809-42d8-a1fe-fd8a60d563b9 · outbound

This paper cites Translation of interviews from a source language to a target language: Examining issues in cross-cultural health care research.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Translation of interviews from a source language to a target language: Examining issues in cross-cultural health care research

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.519641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf418acf-11bd-4324-ae61-94fe09241a25 · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Flamingo: a visual language model for few-shot learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.523648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e1d2b55-3058-47fc-99e3-c4f98f0d5c53 · outbound

This paper cites Nontrivial attractors in a model related to the three-body quantum problem.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Nontrivial attractors in a model related to the three-body quantum problem

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.528108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aad874e7-f21e-467f-a676-e5498d9cec62 · outbound

This paper cites Learning to prune and low-rank adaptation for compact language model deployment.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Learning to prune and low-rank adaptation for compact language model deployment

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.532344Z

Source-reported events for the cited work

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Observation b7ecfb5d-3017-4724-9782-0f012b31d3b0 · outbound

This paper cites The Falcon Series of Open Language Models.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models The Falcon Series of Open Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.536877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e86f27ec-df3e-4a00-a833-e860b1a951d0 · outbound

This paper cites MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.541499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8650ee1-3ead-452a-9eb6-3b9e9df1ff2b · outbound

This paper cites Object detection.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Object detection

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.546214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ff4e0b5-cf2f-4435-aebc-614d1e3f8769 · outbound

This paper cites A comprehensive study of the use of lora in the development of smart cities.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models A comprehensive study of the use of lora in the development of smart cities

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.550007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4028ae28-d4c6-4730-b17d-5ece15759d98 · outbound

This paper cites Composable Sparse Fine-Tuning for Cross-Lingual Transfer.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Composable Sparse Fine-Tuning for Cross-Lingual Transfer

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.554400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc9a0281-a610-4216-9283-56cd4d3a2d81 · outbound

This paper cites Scaling Sparse Fine-Tuning to Large Language Models.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Scaling Sparse Fine-Tuning to Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.558241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd6dabc0-dd7b-429c-85f7-2ec97747c538 · outbound

This paper cites Vqa: Visual question answering.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Vqa: Visual question answering

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.562816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36921024-da49-4ae4-8418-b8d37e03f678 · outbound

This paper cites LLMs for Generation of Architectural Components: An Exploratory Empirical Study in the Serverless World.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models LLMs for Generation of Architectural Components: An Exploratory Empirical Study in the Serverless World

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.567220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e9fdf0c-0485-4140-969c-09d18345aff1 · outbound

This paper cites ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.572789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c951b452-a006-4778-8f2b-ba9e8dfc04ab · outbound

This paper cites MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.578121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2056b28e-f624-44f9-b0c3-cfe601b11a7a · outbound

This paper cites Policy networks, policy communities and the problems of governance.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Policy networks, policy communities and the problems of governance

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.583336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65797b32-675c-4396-b7d0-698d2d309eb6 · outbound

This paper cites Vision in the ganzfeld.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Vision in the ganzfeld

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.588631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9702d413-7601-4abb-a77f-9a350d98e17b · outbound

This paper cites MiniGPT-Reverse-Designing: Predicting Image Adjustments Utilizing MiniGPT-4.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models MiniGPT-Reverse-Designing: Predicting Image Adjustments Utilizing MiniGPT-4

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.593721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 082d4d4a-c89d-4db6-a716-4d75cbb82363 · outbound

This paper cites Understanding Multi-Head Attention in Abstractive Summarization.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Understanding Multi-Head Attention in Abstractive Summarization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.598446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45c7cea0-efc1-4e8c-8c94-e44959071db5 · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Neural Machine Translation by Jointly Learning to Align and Translate

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.603560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6131db07-f85f-4bcc-8c75-f057a7c4ec25 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.607823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65a91a72-87d3-428e-9afc-e30ac39ef9c0 · outbound

This paper cites Computer vision.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Computer vision

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.612719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7907af71-70a4-4f97-9186-6f93d8463aaa · outbound

This paper cites Autoencoders.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Autoencoders

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.616885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8bcc7a8-6914-44d0-aa49-76bc5bebd40f · outbound

This paper cites Simple, Scalable Adaptation for Neural Machine Translation.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Simple, Scalable Adaptation for Neural Machine Translation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.622087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 447fe89f-8295-4f92-8f43-89607fcaa005 · outbound

This paper cites Deepsat: a learning framework for satellite imagery.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Deepsat: a learning framework for satellite imagery

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.626517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94e1eb38-6895-4604-a4f1-af1643ba9790 · outbound

This paper cites Strong baselines for parameter-efficient few-shot fine-tuning.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Strong baselines for parameter-efficient few-shot fine-tuning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.630395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e19c0989-43c6-49fa-a552-0edf01dafcb2 · outbound

This paper cites Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.634601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dccdffc-2ade-4b04-b306-c06d1da31765 · outbound

This paper cites an unresolved cited work.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Unresolved cited work

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.639121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6ab1cbb-aa9a-4ecb-9736-90b2e8410744 · outbound

This paper cites Audio-visual and multimodal speech systems.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Audio-visual and multimodal speech systems

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.643181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74d75183-e199-4623-8901-42a7c9b89ec9 · outbound

This paper cites Semantic parsing on freebase from question-answer pairs.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Semantic parsing on freebase from question-answer pairs

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.648280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 686e4ea7-719a-4132-a02d-b02bfa00dda0 · outbound

This paper cites Alternatives to the Scaled Dot Product for Attention in the Transformer Neural Network Architecture.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Alternatives to the Scaled Dot Product for Attention in the Transformer Neural Network Architecture

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.652726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86f066e4-3ed6-442f-80c7-0898e8ea46ba · outbound

This paper cites Handbook of medical imaging, volume 3.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Handbook of medical imaging, volume 3

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.657488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7526102-c02d-4b11-a973-1b8cb9d94e2e · outbound

This paper cites Deep learning techniques—r-cnn to mask r-cnn: a survey.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Deep learning techniques—r-cnn to mask r-cnn: a survey

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.661867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a6b24bc-59ba-470b-86f8-9bbef363f67c · outbound

This paper cites Low-rank bottleneck in multi-head attention models.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Low-rank bottleneck in multi-head attention models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.666488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9af9a764-64ed-491c-8a29-f571859932a6 · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.671233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18062b57-ae9b-4abf-a2fe-d27c72bc7b41 · outbound

This paper cites Parallel multi-head dot product attention for video summarization.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Parallel multi-head dot product attention for video summarization

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.675807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a637fe17-db28-4528-b742-c9da3360413c · outbound

This paper cites A comprehensive cotton leaf disease dataset for enhanced detection and classification.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models A comprehensive cotton leaf disease dataset for enhanced detection and classification

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.680814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c9709b9-d493-4c3b-8a46-2f8270660e36 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language, 2019.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Piqa: Reasoning about physical commonsense in natural language, 2019

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.684755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cb5abdd-275c-4b1c-99af-598e87f9f1e7 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Piqa: Reasoning about physical commonsense in natural language

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.688921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9df94a9e-ee01-4cf2-b66a-919db0ec728f · outbound

This paper cites Low-Rank Quantization-Aware Training for LLMs.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Low-Rank Quantization-Aware Training for LLMs

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.692975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49931dd1-c51d-448e-8906-a17b8e1e91c5 · outbound

This paper cites Lora for the internet of things.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Lora for the internet of things

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.697302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c52fa85-95d7-442a-9add-df471ff4618b · outbound

This paper cites Satellite image analysis: clustering and classification.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Satellite image analysis: clustering and classification

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.701375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5eb885f-001d-406b-84f1-73fb80623a7b · outbound

This paper cites Food-101–mining discriminative components with random forests.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Food-101–mining discriminative components with random forests

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.705192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 602c6df1-9b7f-4143-923b-654ef9e4991b · outbound

This paper cites High-performance large-scale image recognition without normalization.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models High-performance large-scale image recognition without normalization

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.709483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f25da72-951f-415e-a826-97504a5e3770 · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.714423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation add24221-c297-4fec-9e77-9dece5dbd306 · outbound

This paper cites Instructpix2pix: Learning to follow image editing instructions.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Instructpix2pix: Learning to follow image editing instructions

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.718643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0cee4dc-d62f-4305-91bc-f525cbf9fec6 · outbound

This paper cites Gaia early data release 3-summary of the contents and survey properties.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Gaia early data release 3-summary of the contents and survey properties

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.722697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9253d8f4-5fdb-456e-b81f-a3c963bca306 · outbound

This paper cites Language Models are Few-Shot Learners.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Language Models are Few-Shot Learners

Reference 56

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Observation 210d0ada-64be-4a8b-8ec3-e8c443afa569 · outbound

This paper cites Blip glitches in advanced ligo data.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Blip glitches in advanced ligo data

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.732508Z

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Observation 64623b02-8c92-48eb-a20c-f78a0f5af8bc · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models nuscenes: A multimodal dataset for autonomous driving

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.737638Z

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Observation 28bc4866-1de8-46f9-b640-f35ee9c75348 · outbound

This paper cites Coco-stuff: Thing and stuff classes in context.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Coco-stuff: Thing and stuff classes in context

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.741959Z

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Observation 9b85afd9-07ac-4b8e-aa04-2572c3bb5cca · outbound

This paper cites A practical guide to sentiment analysis, volume 5.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models A practical guide to sentiment analysis, volume 5

Reference 60

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Observation 81865f54-db3d-4312-9b81-1b761226ad1b · outbound

This paper cites an unresolved cited work.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Unresolved cited work

Reference 61

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Observation ae40ed0c-cae1-467b-b350-1a6e02ea1b6d · outbound

This paper cites Ghostvit: Ex- pediting vision transformers via cheap operations.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Ghostvit: Ex- pediting vision transformers via cheap operations

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.755855Z

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Observation bbe2ca6d-1eab-490c-8250-9e75f4ab1c80 · outbound

This paper cites A Short Note on the Kinetics-700 Human Action Dataset.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models A Short Note on the Kinetics-700 Human Action Dataset

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.760080Z

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Observation 1a232bb5-f3ec-4bb3-ad09-bc4979192b82 · outbound

This paper cites Quo vadis, action recognition? a new model and the kinetics dataset.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Quo vadis, action recognition? a new model and the kinetics dataset

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.765135Z

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Observation cb67f995-bdb5-4ff8-82bb-8c0c4f3179a9 · outbound

This paper cites Introduction to the conll-2005 shared task: Semantic role labeling.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Introduction to the conll-2005 shared task: Semantic role labeling

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.769274Z

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Observation e2d87bcb-3594-48b3-8058-e3c8eec8ea2f · outbound

This paper cites SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.773388Z

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Observation a1101601-3c7e-47af-8913-14ce2aecfc4c · outbound

This paper cites Ladder fine-tuning approach for sam integrating complementary network.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Ladder fine-tuning approach for sam integrating complementary network

Reference 67

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Observation b6a2f110-450a-4c14-b74c-d7b261a6c93c · outbound

This paper cites Domain- specific batch normalization for unsupervised domain adaptation.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Domain- specific batch normalization for unsupervised domain adaptation

Reference 68

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Observation d4e7a7cc-4983-45c3-b058-ddff79674995 · outbound

This paper cites Is the number of trainable parameters all that actually matters? In I (Still) Can’t Believe It’s Not Better! Workshop at NeurIPS 2021, pages 27–32.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Is the number of trainable parameters all that actually matters? In I (Still) Can’t Believe It’s Not Better! Workshop at NeurIPS 2021, pages 27–32

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.787080Z

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Observation c7a1b555-818d-45c8-a123-944277037ab4 · outbound

This paper cites Convolutional neural network (cnn) for image detection and recognition.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Convolutional neural network (cnn) for image detection and recognition

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.791257Z

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Observation cabe5227-1240-49b7-aedd-39d212e2ad63 · outbound

This paper cites Understanding and improving visual prompting: A label-mapping perspective.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Understanding and improving visual prompting: A label-mapping perspective

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.796221Z

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Observation dfb5a287-51bf-46f7-be46-9f9f6f8500ef · outbound

This paper cites Aggregate, Decompose, and Fine-Tune: A Simple Yet Effective Factor-Tuning Method for Vision Transformer.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Aggregate, Decompose, and Fine-Tune: A Simple Yet Effective Factor-Tuning Method for Vision Transformer

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.800455Z

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Observation 37884e24-b6af-41a2-b134-01edd2c14a7e · outbound

This paper cites Conv-adapter: Exploring parameter efficient transfer learning for convnets.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Conv-adapter: Exploring parameter efficient transfer learning for convnets

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.805690Z

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Observation 4909980b-7421-4581-9a19-60edd20c964e · outbound

This paper cites Learning Coordinated Bimanual Manipulation Policies using State Diffusion and Inverse Dynamics Models.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Learning Coordinated Bimanual Manipulation Policies using State Diffusion and Inverse Dynamics Models

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:59:50.004075Z

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Observation 0b937b42-48b0-454e-89e5-81ace1abefb6 · outbound

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

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Parameter-Efficient Fine-Tuning Design Spaces

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.815630Z

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Observation 2a047c91-03ce-4649-afab-9c0e0f1f332f · outbound

This paper cites Developing prefix-tuning models for hierarchical text classification.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Developing prefix-tuning models for hierarchical text classification

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.820758Z

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Observation 49b6d4b3-6492-4a7f-bdbd-61d05fad435a · outbound

This paper cites Review of image classification algorithms based on convolutional neural networks.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Review of image classification algorithms based on convolutional neural networks

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.825196Z

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Observation 62e7f3c5-991d-40ca-ba5d-1272fe4a7117 · outbound

This paper cites PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.829613Z

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Observation 68612985-4b08-44b6-9ca3-97d9360ff6ad · outbound

This paper cites Sharegpt4v: Improving large multi-modal models with better captions.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Sharegpt4v: Improving large multi-modal models with better captions

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.834738Z

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Observation 40f2a2b8-bd1f-48be-8f56-a216b0546c3f · outbound

This paper cites Dynamic load balancing on single-and multi-gpu systems.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Dynamic load balancing on single-and multi-gpu systems

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.839058Z

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Observation 8e7d12bb-3588-4912-bccb-c29146549f50 · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recognition.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Adaptformer: Adapting vision transformers for scalable visual recognition

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.843974Z

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Observation 1531169f-8e10-4a18-b035-8e51dad32730 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models A simple framework for contrastive learning of visual representations

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.848475Z

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Observation b2c86417-a881-4739-a1d2-d886a7cdf3a7 · outbound

This paper cites Audio-visual integration in multimodal communication.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Audio-visual integration in multimodal communication

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.853378Z

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Observation 8d6fb34b-6466-42c5-b66d-86e53ee38222 · outbound

This paper cites PaLI: A Jointly-Scaled Multilingual Language-Image Model.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models PaLI: A Jointly-Scaled Multilingual Language-Image Model

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.858053Z

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Observation 982ceabd-04cf-47b4-8987-b842285668d7 · outbound

This paper cites Multi-Source Cross-Lingual Model Transfer: Learning What to Share.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Multi-Source Cross-Lingual Model Transfer: Learning What to Share

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.862676Z

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Observation 40e10abd-a517-4b1c-b34c-23b2134c311a · outbound

This paper cites TigerBot: An Open Multilingual Multitask LLM.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models TigerBot: An Open Multilingual Multitask LLM

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.867095Z

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Observation 124554af-6fbd-4d0c-8820-4d1e55a7803a · outbound

This paper cites End-to-end multi-modal video temporal grounding.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models End-to-end multi-modal video temporal grounding

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.872092Z

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Observation aafd5f49-1715-4afa-befb-bfdba66f406c · outbound

This paper cites Efficient Mixed-Precision Quantization of Deep Neural Networks for Edge Applications.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Efficient Mixed-Precision Quantization of Deep Neural Networks for Edge Applications

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.876114Z

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Observation db515f45-cb84-49fa-a1f3-d71f2753868b · outbound

This paper cites Hadamard adapter: An extreme parameter-efficient adapter tuning method for pre-trained language models.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Hadamard adapter: An extreme parameter-efficient adapter tuning method for pre-trained language models

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.880273Z

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Observation 32ba8a5b-2189-48c9-b3e8-9fe0bf392d65 · outbound

This paper cites Vision Transformer Adapter for Dense Predictions.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Vision Transformer Adapter for Dense Predictions

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.885581Z

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Observation 039210fd-39d2-4215-ac61-becb7b35b429 · outbound

This paper cites Remote sensing image scene classification: Bench- mark and state of the art.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Remote sensing image scene classification: Bench- mark and state of the art

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.890173Z

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Observation 456cf7e4-dc13-4ac1-b1ab-7ab7cdccd604 · outbound

This paper cites Flamingo.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Flamingo

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.895426Z

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Observation 01cbf6b1-d069-422c-b82a-2c68c9136ceb · outbound

This paper cites Task Arithmetic with LoRA for Continual Learning.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Task Arithmetic with LoRA for Continual Learning

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.899922Z

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Observation 4c627915-296f-4af1-9898-6d4311d98e87 · outbound

This paper cites Smop: Towards efficient and effective prompt tuning with sparse mixture-of-prompts.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Smop: Towards efficient and effective prompt tuning with sparse mixture-of-prompts

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.904623Z

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Observation ef59e86d-91e3-4ad4-a2ea-2fd25ce62074 · outbound

This paper cites Dramaqa: Character-centered video story understanding with hierarchical qa.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Dramaqa: Character-centered video story understanding with hierarchical qa

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.909432Z

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Observation ce84bcc0-1292-4ecd-91d0-684c7c621495 · outbound

This paper cites Natural language processing.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Natural language processing

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.913772Z

Source-reported events for the cited work

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Observation 08d8c762-7607-41ca-8e43-a7258689e53a · outbound

This paper cites Unifying molecular and textual representations via multi-task language modelling.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Unifying molecular and textual representations via multi-task language modelling

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.919057Z

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Observation 2733dda3-948c-4deb-83e2-6fe87833917a · outbound

This paper cites AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.924203Z

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Observation e335b4eb-d20a-48ff-bbaa-790f341b1c55 · outbound

This paper cites How fine-tuning allows for effective meta-learning.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models How fine-tuning allows for effective meta-learning

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.929644Z

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Observation dc1ebd36-8cd6-49f3-90b4-1492d4da2274 · outbound

This paper cites Use gpt-j prompt generation with roberta for ner models on diagnosis extraction of periodontal diagnosis from electronic dental records.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models Use gpt-j prompt generation with roberta for ner models on diagnosis extraction of periodontal diagnosis from electronic dental records

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:47.934140Z

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Pith citing papers

Observation 8c7a03cf-2649-47ee-ba7c-d1c89ec8c348 · inbound

Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection cites this paper.

Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T20:36:34.168422Z

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Observation 433d54a1-1762-4195-92ea-b732936ef8f6 · inbound

PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark cites this paper.

PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-17T05:09:03.645254Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b991ba69-6afa-454e-a4b3-199036dd9972 · inbound

ASA: Backbone-Training-Free Representation Engineering for Tool-Calling Agents cites this paper.

ASA: Backbone-Training-Free Representation Engineering for Tool-Calling Agents PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T04:36:51.421581Z

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Observation ce09e7d1-0c8e-44a6-8051-88302fee0bed · inbound

LoCA: Spatially-Aware Low-Rank Convolutional Adaptation of Vision Foundation Models cites this paper.

LoCA: Spatially-Aware Low-Rank Convolutional Adaptation of Vision Foundation Models PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models

Reference 33

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T23:16:36.725765Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.