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Paper Citation Record · LEDGER

FineGates: LLMs Finetuning with Compression using Stochastic Gates

As of 15 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2412.12951.

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

pith.paper-citation-record.v1
2412.12951 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:37:39.109971Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

21 of 21 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bb345255-2aad-4735-83ba-1ce1393706c9 · outbound

This paper cites LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters.

FineGates: LLMs Finetuning with Compression using Stochastic Gates LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:37:38.959932Z digest=sha256:e51d0b9aaf511df84e321a5236e3c1ff447d2c93b1b61e44d52e2034a1ebb3af

Observation d611bb79-8524-4824-a725-3b8f8a684f6b · outbound

This paper cites One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning.

FineGates: LLMs Finetuning with Compression using Stochastic Gates One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning

Reference 3

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source=pdf_text observed=2026-08-11T13:37:38.975262Z digest=sha256:52ae09a4c31260389d3977e78edd4ae9b626452de8324b5fdb74a50c7657f688

Observation fae90cd4-f1a1-4ce9-a1b3-424393506691 · outbound

This paper cites SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters.

FineGates: LLMs Finetuning with Compression using Stochastic Gates SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters

Reference 5

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source=pdf_text observed=2026-08-11T13:37:38.989122Z digest=sha256:5ac7f8fc198f5ffa304c5f0a7fcf787d341643ea1bcea848f1296bd0e7276cb1

Observation 70ba8ea8-e90b-49c6-a122-d4831967591a · outbound

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

FineGates: LLMs Finetuning with Compression using Stochastic Gates The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 9

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source=pdf_text observed=2026-08-11T13:37:39.025487Z digest=sha256:f3166e1890f8ef46e8872b38292d5d4fcba9bd5f1c8fea45fef7af04750ba600

Observation 43228dba-83be-4515-b305-06cf25f23d0a · outbound

This paper cites Parameter-Efficient Sparsity for Large Language Models Fine-Tuning.

FineGates: LLMs Finetuning with Compression using Stochastic Gates Parameter-Efficient Sparsity for Large Language Models Fine-Tuning

Reference 11

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source=pdf_text observed=2026-08-11T13:37:39.038878Z digest=sha256:2c34854e8e289792d497fba984c31b1b0db55b7578f1c59592e7bb671b8bf1b6

Observation 9534321c-2b14-4854-b86e-977c9c0ecf84 · outbound

This paper cites NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models.

FineGates: LLMs Finetuning with Compression using Stochastic Gates NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models

Reference 12

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source=pdf_text observed=2026-08-11T13:37:39.044748Z digest=sha256:5674155f8fa692fc7196cd29ab4a6dc54ecdfced17790c299a3e06b00ae2a49d

Observation 22da5c8f-a4da-46f6-a705-eeeca38ae19b · outbound

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

FineGates: LLMs Finetuning with Compression using Stochastic Gates RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 13

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source=pdf_text observed=2026-08-11T13:37:39.051681Z digest=sha256:d8cf8531ec3b615849519f8a84c9758fa0b6745babc687953b7ee454b8a28fa9

Observation 7900e55e-a855-4440-96ea-a5c8f17129ff · outbound

This paper cites Decoupled Weight Decay Regularization.

FineGates: LLMs Finetuning with Compression using Stochastic Gates Decoupled Weight Decay Regularization

Reference 14

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source=pdf_text observed=2026-08-11T13:37:39.058919Z digest=sha256:1846fe705c86f3257f30cb16e37bc1ecb0608be2ee03842b73f3e17c6f5d1447

Observation 5969d639-2c32-4121-a031-fc2e550a8b9f · outbound

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

FineGates: LLMs Finetuning with Compression using Stochastic Gates AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Reference 15

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source=pdf_text observed=2026-08-11T13:37:39.065819Z digest=sha256:0acbcbe51075ba82dcb2cb59d892780adb5bf39bc7e744e9a2a96900199f9691

Observation 60fb603f-49ca-4d9e-a071-01d9c8b8f477 · outbound

This paper cites Knowledge Editing in Language Models via Adapted Direct Preference Optimization.

FineGates: LLMs Finetuning with Compression using Stochastic Gates Knowledge Editing in Language Models via Adapted Direct Preference Optimization

Reference 17

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source=pdf_text observed=2026-08-11T13:37:39.085414Z digest=sha256:a4bc9979f06be2db5b39e53f24f7d113a293ac39ecfdebd6584d904faa1e5467

Observation edd36266-ceea-4be3-a9e3-f06f940be680 · outbound

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

FineGates: LLMs Finetuning with Compression using Stochastic Gates QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Reference 19

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source=pdf_text observed=2026-08-11T13:37:39.098228Z digest=sha256:eb1828a18685b37c967bbfb2eadd5a470aef17d552579a2f755751355e220353

Observation 4cdab6a2-10f5-4154-b204-6b905bddb292 · outbound

This paper cites LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning.

FineGates: LLMs Finetuning with Compression using Stochastic Gates LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning

Reference 20

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source=pdf_text observed=2026-08-11T13:37:39.104166Z digest=sha256:01d8894915a109d017303b4cf48967fb1003ddd78b949ef958ef2f5acedf62b0

Observation cbffbc6e-f309-4b74-9466-41507d492341 · outbound

This paper cites APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference.

FineGates: LLMs Finetuning with Compression using Stochastic Gates APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference

Reference 21

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source=pdf_text observed=2026-08-11T13:37:39.109971Z digest=sha256:11897899fb16153149b8ed6fb03ae75764cb368a56ac799d499cef5532278937

Observation 96c5b90a-b226-431f-b89f-30b89c2db25e · outbound

This paper cites Structured Pruning Learns Compact and Accurate Models.

FineGates: LLMs Finetuning with Compression using Stochastic Gates Structured Pruning Learns Compact and Accurate Models

Reference 2016

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source=pdf_text observed=2026-08-11T13:37:39.091398Z digest=sha256:c526182aa94af22cb3f3582d130bffa5e3180428f5350fca4cb772fbc71dbf9d

Observation c54b1ee3-738c-4924-8ee5-168f49988073 · outbound

This paper cites VeRA: Vector-based Random Matrix Adaptation.

FineGates: LLMs Finetuning with Compression using Stochastic Gates VeRA: Vector-based Random Matrix Adaptation

Reference 2017

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source=pdf_text observed=2026-08-11T13:37:39.015019Z digest=sha256:a5d596814dac8638ebb3f0c36e3b5c9c74a0c470de2df94b36669b5292c97aac

Observation e566352a-b8d7-4344-a89e-df74ff9a09fd · outbound

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

FineGates: LLMs Finetuning with Compression using Stochastic Gates LoRA: Low-Rank Adaptation of Large Language Models

Reference 2019

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source=pdf_text observed=2026-08-11T13:37:39.008120Z digest=sha256:2ba4515b2edf3488bceca5e3a09b475f52498b5f86104769ad728edcfb330d16

Observation b6ea905d-3e74-4795-b540-3fd15a901fe5 · outbound

This paper cites Adarankgrad: Adaptive gradient-rank and moments for memory-efficient llms training and fine-tuning.

FineGates: LLMs Finetuning with Compression using Stochastic Gates Adarankgrad: Adaptive gradient-rank and moments for memory-efficient llms training and fine-tuning

Reference 2020

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source=pdf_text observed=2026-08-11T13:37:39.073074Z digest=sha256:21c76293bf5e47e8477f25cec3ce0dd2e96eb6c8c681b1bc4d50f272b9ae70ed

Observation 80f7dbd3-0b05-4750-ac40-522008e61992 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

FineGates: LLMs Finetuning with Compression using Stochastic Gates Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 2021

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source=pdf_text observed=2026-08-11T13:37:39.032181Z digest=sha256:ec78d91de45855c7c5181a6021a6780171c1fbf6ef113009166bcc22114d9108

Observation 1c609644-d4c2-46a8-bfaf-0397e417528d · outbound

This paper cites Distilling the Knowledge in a Neural Network.

FineGates: LLMs Finetuning with Compression using Stochastic Gates Distilling the Knowledge in a Neural Network

Reference 2022

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source=pdf_text observed=2026-08-11T13:37:38.999966Z digest=sha256:08d63ef2bfa42288b4b007810c6b351f352086b76e3b4d7eb3581f5bb1f37c18

Observation f4a3c265-805d-4a26-ad1d-978f1e2a96e0 · outbound

This paper cites Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models.

FineGates: LLMs Finetuning with Compression using Stochastic Gates Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models

Reference 2023

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source=pdf_text observed=2026-08-11T13:37:38.982037Z digest=sha256:f91e48fae8a2bab447d2841bb8d347b701c230dcc68bbdbb522a3f38535305f8

Observation d150909c-3950-405c-8c12-b12358e4a58a · outbound

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

FineGates: LLMs Finetuning with Compression using Stochastic Gates Low-Rank Quantization-Aware Training for LLMs

Reference 2024

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:37:38.968326Z digest=sha256:1f8c47f85554c90521fc39bd498565d87737891a6e24effc94ebdd3cd6b117da

Pith citing papers

No inbound Pith citation observations are available.