Paper Citation Record · LEDGER

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:2501.18475.

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

pith.paper-citation-record.v1
2501.18475 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T23:25:54.763519Z

measured 36 of 36 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T19:10:58.845006Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T21:06:14.418560Z

Reference resolution

34 of 34 outbound references displayed

verified exact1
verified fuzzy2
unresolved31
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 885e6a86-93df-4c1f-bdfb-3e81b0923770 · outbound

This paper cites QuantEase: Optimization-based Quantization for Language Models.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization QuantEase: Optimization-based Quantization for Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.600693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9e48516-a85a-4377-8732-60bb2b9eb9b4 · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.611967Z

Source-reported events for the cited work

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Observation b99beb5f-de6c-474e-9efe-ec9e43ddcf1c · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Training Verifiers to Solve Math Word Problems

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.628098Z

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Unavailable: canonical work link unavailable.

Observation 6a55fa05-375f-4956-8652-549af3ebbc2f · outbound

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

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization LoRA: Low-Rank Adaptation of Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.649320Z

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Unavailable: canonical work link unavailable.

Observation d26bfd13-05ca-4ee6-a4f3-1cd29fdbc1ea · outbound

This paper cites LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.654512Z

Source-reported events for the cited work

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Observation 3223d2ca-3414-498b-b0f7-80b96282dcb5 · outbound

This paper cites Mistral 7B.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Mistral 7B

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.659609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abbb87e5-cbc8-409c-b783-6f0485598551 · outbound

This paper cites Mawps: A math word problem repository.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Mawps: A math word problem repository

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.664544Z

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Unavailable: canonical work link unavailable.

Observation 3a7dc95f-8886-4098-b396-fb5cc10540bb · outbound

This paper cites LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.674492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 561f9e6f-29ca-478c-8bca-35f14510a27d · outbound

This paper cites Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.679881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad512f46-2136-4a7f-8be7-210d0e2234b3 · outbound

This paper cites Decoupled Weight Decay Regularization.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Decoupled Weight Decay Regularization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.689805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4422294a-dcf1-4c9c-bfae-3ba4d3e4d092 · outbound

This paper cites Pointer Sentinel Mixture Models.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Pointer Sentinel Mixture Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.694842Z

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Unavailable: canonical work link unavailable.

Observation f2cf3bd9-12ea-4ea3-8a4b-afafa3ef4b90 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.700012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81b44fd4-26c9-499d-a434-7dac2eb6be13 · outbound

This paper cites Are NLP Models really able to Solve Simple Math Word Problems?.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Are NLP Models really able to Solve Simple Math Word Problems?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.704986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 699b2112-e378-464f-9b71-200ee91796c7 · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization SocialIQA: Commonsense Reasoning about Social Interactions

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.709780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c9d6478-6e43-446e-a317-3624827d4102 · outbound

This paper cites OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.715241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae3df061-f198-4be6-bcbd-fef63154ddbe · outbound

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

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.720016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 567223a1-6a0c-49e3-9e9b-e05fb30b36e5 · outbound

This paper cites RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.724690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77b962e6-f603-4aa6-935c-5c150fe21e99 · outbound

This paper cites LoRA-GA: Low-Rank Adaptation with Gradient Approximation.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.729617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dae832e8-680d-4d9d-b77b-e0f4a1f3b475 · outbound

This paper cites ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.734518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ee09635-d575-4d3c-8ecd-3ef489f995c9 · outbound

This paper cites Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-09T23:25:54.839680Z

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 8dcd9155-3d1c-4108-a012-1a55e9d8140b · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.743907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66848603-ace9-4f60-8543-f243625a1f1e · outbound

This paper cites COMQ: A Backpropagation-Free Algorithm for Post-Training Quantization.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization COMQ: A Backpropagation-Free Algorithm for Post-Training Quantization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.749430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0115d7a-e950-413f-810d-203a3114f72a · outbound

This paper cites Multiple task Following the framework proposed by Hu et al.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Multiple task Following the framework proposed by Hu et al

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:25:55.305822Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c6a1093f-108d-4d69-9ba7-8d4f4b3a9e55 · outbound

This paper cites an unresolved cited work.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-09T23:25:55.289625Z

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 c7f5ddc8-ef34-4cbb-aba4-5999d39a584c · outbound

This paper cites 16 Published in Transactions on Machine Learning Research (08/2025) Table 11: Hyper-parameter for the finetuning of Llama2.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization 16 Published in Transactions on Machine Learning Research (08/2025) Table 11: Hyper-parameter for the finetuning of Llama2

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:25:55.273333Z

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 a054d16f-d625-4c90-b89c-24b86033cb49 · outbound

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

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 1936

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.638946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bfc115f-2319-4bef-93ed-2ddbc5b2e46f · outbound

This paper cites On the Crucial Role of Initialization for Matrix Factorization.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization On the Crucial Role of Initialization for Matrix Factorization

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.669559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1eac84f-ef7c-4762-9ded-a8aabe35e62a · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.685028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0d968b9-c366-46cc-9bc5-5ba728df7430 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.617311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91ae33e6-8d75-46fb-949a-84ca7b7b57c9 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.622902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e104309-e2c0-4707-8e22-21abd23b6db3 · outbound

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

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.606104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdf64f87-139a-4a4d-b0f3-1fe4e7269b0f · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization QLoRA: Efficient Finetuning of Quantized LLMs

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.633860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaa54dd3-4829-4916-bb43-238c76bb5230 · outbound

This paper cites GPT-4 Technical Report.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization GPT-4 Technical Report

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.594753Z

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Unavailable: canonical work link unavailable.

Observation 7f997571-ba7c-45c3-ac2e-a42b34c81b1d · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.643828Z

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Pith citing papers

Observation cc3c6953-16bf-4112-ac22-bfdcd6b34373 · inbound

ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression cites this paper.

ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-06-28T19:12:34.514520Z

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 06f4f22b-a193-4b9f-9bdf-d5a6f42a51d4 · inbound

GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation cites this paper.

GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:06:14.420621Z

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.