Paper Citation Record · LEDGER

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization

As of 16 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2412.06858.

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

pith.paper-citation-record.v1
2412.06858 v2

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:08:46.997688Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

27 of 27 outbound references displayed

verified exact0
verified fuzzy11
unresolved14
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2a526b3a-3500-4868-8d49-e035f6d06bf2 · outbound

This paper cites Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:08:48.351933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4a9f2c71-0722-4369-9645-28bda3cb6cfc · outbound

This paper cites EfficientQAT: Efficient Quantization-Aware Training for Large Language Models.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization EfficientQAT: Efficient Quantization-Aware Training for Large Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T20:08:46.495869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a3a10fd-8c3f-4aa1-b006-cc925dfd3ed3 · outbound

This paper cites Palm: Scaling language modeling with pathways.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Palm: Scaling language modeling with pathways

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:08:48.337276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 58eadb53-94f2-441d-aa35-c24ac91c7b03 · outbound

This paper cites an unresolved cited work.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:08:48.233554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 80d256e7-c182-4b8f-9e6c-b52da9f94639 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Qlora: Efficient finetuning of quantized llms

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:08:48.114965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 08846aa6-8fbe-4506-b235-386758d2b3a4 · outbound

This paper cites SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T20:08:46.533253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bab8d1ba-3755-4dc5-9975-6abaeb4c3042 · outbound

This paper cites Hawq-v2: Hessian aware trace-weighted quantization of neural networks.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Hawq-v2: Hessian aware trace-weighted quantization of neural networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:08:48.098313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 42263eb2-4dbf-4a41-8ed7-9c5f5de4af89 · outbound

This paper cites Hawq: Hessian aware quantization of neural networks with mixed-precision.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Hawq: Hessian aware quantization of neural networks with mixed-precision

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:08:48.019653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0bee0ec6-1ed2-4e86-999d-400f46f30a90 · outbound

This paper cites BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T20:08:46.548503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a41970a-da90-4c41-a5ae-69b80e401c11 · outbound

This paper cites Glam: Efficient scaling of language models with mixture-of-experts.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Glam: Efficient scaling of language models with mixture-of-experts

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:08:47.874327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e596bede-f1c0-4153-8d47-0d4c9d829032 · outbound

This paper cites Gpt-3: Its nature, scope, limits, and consequences.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Gpt-3: Its nature, scope, limits, and consequences

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:08:47.859164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5c99e64a-f436-491f-841d-4541fe62a5f2 · outbound

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

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T20:08:46.613246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ee47ec4-2c98-4e3b-b342-31f0d9b3f26f · outbound

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

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization LoRA: Low-Rank Adaptation of Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T20:08:46.618543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d057e549-ff21-4087-b82d-779ce0d7bf02 · outbound

This paper cites Scaling Laws for Neural Language Models.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Scaling Laws for Neural Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T20:08:46.623021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04f5fb6c-f226-4659-bf54-4ba5affb2bb7 · outbound

This paper cites SqueezeLLM: Dense-and-Sparse Quantization.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization SqueezeLLM: Dense-and-Sparse Quantization

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T20:08:46.631080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98080bb7-ab6b-4e0e-b1bc-6a1ad05638b1 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:08:47.844795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6db3ee7a-ba31-4cb4-a21d-02ead5086f58 · outbound

This paper cites LLM-QAT: Data-Free Quantization Aware Training for Large Language Models.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T20:08:46.640538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cd7b284-004d-4b75-9720-a410207d62f8 · outbound

This paper cites Pointer Sentinel Mixture Models.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Pointer Sentinel Mixture Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T20:08:46.681916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8795a43-f225-4e0f-b23c-2d6493c87f87 · outbound

This paper cites Mixed-Precision Training Guide, 2023.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Mixed-Precision Training Guide, 2023

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:08:47.672988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8cf13f8a-dfcc-40a8-905d-4037862a1393 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T20:08:46.860105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 743047ae-22e0-4716-b5d8-73bd468cb879 · outbound

This paper cites Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T20:08:46.864896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b901b2b4-a946-474b-a381-d3ef804264c6 · outbound

This paper cites Open and efficient foundation language models.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Open and efficient foundation language models

Reference 22

Resolution: malformed identifier; no resolver link, observed 2026-08-11T20:08:46.869237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce8544ab-bc0a-48a4-b183-f24caffdd3ee · outbound

This paper cites Outlier suppression: Pushing the limit of low-bit transformer language models.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Outlier suppression: Pushing the limit of low-bit transformer language models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:08:47.537701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3118e128-a998-4bdb-a45e-19a50c848867 · outbound

This paper cites BLOOM: A 176B-Parameter Open-Access Multilingual Language Model.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T20:08:46.877482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8732d66-24e1-4a55-99c4-e0b334d27a80 · outbound

This paper cites Hero: Hessian-enhanced robust optimization for unifying and improving generalization and quantization performance.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Hero: Hessian-enhanced robust optimization for unifying and improving generalization and quantization performance

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:08:47.523298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation af19ebc8-7d21-49ce-bc27-703578ab6070 · outbound

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

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization OPT: Open Pre-trained Transformer Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T20:08:46.886544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adbc729f-9b7b-4ad5-aa0c-3d49ba55ff20 · outbound

This paper cites Table 10: OPT-1.3B-4bit latency(s) for generating different lengths of tokens.

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization Table 10: OPT-1.3B-4bit latency(s) for generating different lengths of tokens

Reference 27

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T20:08:47.485547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.