Paper Citation Record · LEDGER

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation

As of 8 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2508.16191.

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

pith.paper-citation-record.v1
2508.16191 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:33:17.278546Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

46 of 46 outbound references displayed

verified exact5
verified fuzzy8
unresolved33
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3f608c5b-67aa-4a15-8bda-57f44af4bdf0 · outbound

This paper cites Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language Models.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language Models

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:33:18.651844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ced5fa40-a84e-4dac-a45d-b66940ff799c · outbound

This paper cites Program Synthesis with Large Language Models.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Program Synthesis with Large Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:12.839802Z

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Observation 241aac14-8c28-42f1-a502-42a0da460e2f · outbound

This paper cites Language Models are Few-Shot Learners.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Language Models are Few-Shot Learners

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:12.919440Z

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Observation 0dd9c966-b57e-4491-a159-9a0eef6c84f5 · outbound

This paper cites Boolq: Exploring the surprising difficulty of natural yes/no questions.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:33:19.901950Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6fb2da2f-5be9-48d8-9a22-bee4c867411b · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Training Verifiers to Solve Math Word Problems

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:13.187428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31533800-2b6d-4326-82dc-8963d50809ac · outbound

This paper cites The pascal recognising textual entailment challenge.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation The pascal recognising textual entailment challenge

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:33:19.741861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d66a7e00-af97-493b-aaa5-77317cd53982 · outbound

This paper cites Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:13.475037Z

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Observation bef0ac6b-7616-406b-9f20-d081a0622cfe · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation QLoRA: Efficient Finetuning of Quantized LLMs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:13.567444Z

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Observation dd989852-0977-45ed-b658-fe7f01c8d078 · outbound

This paper cites Sparse Low-rank Adaptation of Pre-trained Language Models.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Sparse Low-rank Adaptation of Pre-trained Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:13.657154Z

Source-reported events for the cited work

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Observation 41924d27-c112-44be-a263-08c62fac779e · outbound

This paper cites Xo RA : Expander adapted lo RA finetuning.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Xo RA : Expander adapted lo RA finetuning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:33:19.581371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4c022bbc-f032-41fa-91e3-065d2a571fb5 · outbound

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

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:13.812258Z

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Observation d141022c-5142-4941-8199-f66c33bf4c97 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:13.957240Z

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Observation 9b212754-99a6-4e22-9e82-06e0e8796ff4 · outbound

This paper cites Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:33:18.411079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cf19e553-4b57-4e17-a109-30cdf526e666 · outbound

This paper cites Textbooks Are All You Need.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Textbooks Are All You Need

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:14.227984Z

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Observation 2020c9fd-ada6-409f-8cb8-28bc2d8010cc · outbound

This paper cites Parameter-Efficient Transfer Learning with Diff Pruning.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Parameter-Efficient Transfer Learning with Diff Pruning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:14.305960Z

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Observation f84976e3-5880-4030-b5df-5eae5bd0db83 · outbound

This paper cites Gora: Gradient-driven adaptive low rank adaptation, 2025.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Gora: Gradient-driven adaptive low rank adaptation, 2025

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:14.418031Z

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Observation 1f39d57b-a4ec-4a68-9ccd-e4167c157e22 · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Parameter-Efficient Transfer Learning for NLP

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:14.531714Z

Source-reported events for the cited work

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Observation 2782ce0b-c017-4c80-b781-fae6510f1aae · outbound

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

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation LoRA: Low-Rank Adaptation of Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:14.677479Z

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Observation c7619741-a3ca-4874-b73b-471540d4e686 · outbound

This paper cites Looking beyond the surface: A challenge set for reading comprehension over multiple sentences.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Looking beyond the surface: A challenge set for reading comprehension over multiple sentences

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:33:19.440864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a3e8dee5-fe8a-438e-9055-3f3d7d882b2e · outbound

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

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation VeRA: Vector-based Random Matrix Adaptation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:14.836891Z

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Observation 0bd1e196-8672-4d1c-8baf-edddea6c96e5 · outbound

This paper cites Enhancing Large Language Model Performance with Gradient-Based Parameter Selection.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Enhancing Large Language Model Performance with Gradient-Based Parameter Selection

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:33:18.081272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c5fb43a1-b53e-439a-9164-8b75a9a19101 · outbound

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

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:15.028510Z

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Unavailable: canonical work link unavailable.

Observation 0732a16b-1893-48b9-b5d2-801deaf9f16a · outbound

This paper cites AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:15.123076Z

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Observation a26cd787-9830-44fe-b122-55eefd1ccc8d · outbound

This paper cites ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:15.257502Z

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Observation 7559f462-a32c-4ec9-94ac-aab7bc99d807 · outbound

This paper cites Compacter: Efficient Low-Rank Hypercomplex Adapter Layers.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Compacter: Efficient Low-Rank Hypercomplex Adapter Layers

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:15.367652Z

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Observation 7152f900-e565-4513-a333-ba2956dd8b4d · outbound

This paper cites Phi-2: The surprising power of small language models.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Phi-2: The surprising power of small language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:33:19.238284Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aef78e1b-9790-41ad-aaa5-df99563c07a0 · outbound

This paper cites AdapterHub: A Framework for Adapting Transformers.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation AdapterHub: A Framework for Adapting Transformers

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:15.503670Z

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Observation 5dae3360-0e9e-437a-ba48-5806fcc023ec · outbound

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

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:15.587239Z

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Observation 3936f051-f4df-45b4-9a43-772ae8e95a15 · outbound

This paper cites WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:15.702574Z

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Unavailable: canonical work link unavailable.

Observation fca64d7e-309f-465c-9613-20d36c42d629 · outbound

This paper cites Know what you don ' t know: Unanswerable questions for SQ u AD.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Know what you don ' t know: Unanswerable questions for SQ u AD

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:15.833300Z

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Unavailable: canonical work link unavailable.

Observation eddb13b5-baca-4bb0-8d00-31d9ced6898e · outbound

This paper cites Choice of plausible alternatives: An evaluation of commonsense causal reasoning.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Choice of plausible alternatives: An evaluation of commonsense causal reasoning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:33:19.106306Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cc04bbf7-e983-4857-bf1c-0fcb435fabe5 · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Recursive deep models for semantic compositionality over a sentiment treebank

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:33:18.972778Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9b6f2c04-0c80-4605-be26-8b12d05f768a · outbound

This paper cites Sparse is Enough in Fine-tuning Pre-trained Large Language Models.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Sparse is Enough in Fine-tuning Pre-trained Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:16.066343Z

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Observation 685150dd-9b59-4131-8ee2-ed93020e951f · outbound

This paper cites Training Neural Networks with Fixed Sparse Masks.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Training Neural Networks with Fixed Sparse Masks

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:16.131863Z

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Observation 1e07e83f-675d-4898-b249-103416c0ed7e · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation LLaMA: Open and Efficient Foundation Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:16.223836Z

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Observation 1aa5aebf-6db9-42aa-a25b-a2674c9a7f1a · outbound

This paper cites Glue: A multi-task benchmark and analysis platform for natural language understanding.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Glue: A multi-task benchmark and analysis platform for natural language understanding

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:33:18.818692Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 77590faa-bb67-4bab-84b7-a5374d3df39a · outbound

This paper cites SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:16.462429Z

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Observation bcb6d216-929c-4819-a916-c6c6a1d2d1ed · outbound

This paper cites Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:16.547782Z

Source-reported events for the cited work

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Observation 98375c99-8f46-4302-bc8e-676094bc1b9f · outbound

This paper cites Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:16.622932Z

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Unavailable: canonical work link unavailable.

Observation 84caad95-cdb8-415f-8e38-f0b3af41e05c · outbound

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

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:33:17.784963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f33e3494-a4ee-4963-a2ba-a15af7b04205 · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models, 2022.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models, 2022

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:16.793882Z

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Unavailable: canonical work link unavailable.

Observation 73a82e9a-bf96-4f26-b2c0-8f502bab158e · outbound

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

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:16.928324Z

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Observation 21b668b0-6030-4e0b-b14c-409376b230ca · outbound

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

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:17.053712Z

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Observation cda02456-f76a-4a96-a948-ecfb0cf2c591 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation OPT: Open Pre-trained Transformer Language Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:17.084635Z

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Unavailable: canonical work link unavailable.

Observation 377bd2eb-7d7c-4b5e-b12c-445cb009b6ce · outbound

This paper cites Gradient-based Parameter Selection for Efficient Fine-Tuning.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Gradient-based Parameter Selection for Efficient Fine-Tuning

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:33:17.487998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1d975d77-176e-4ae7-91d5-b8c5e078fcff · outbound

This paper cites Masking as an Efficient Alternative to Finetuning for Pretrained Language Models.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Masking as an Efficient Alternative to Finetuning for Pretrained Language Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:17.278546Z

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Pith citing papers

No inbound Pith citation observations are available.