Paper Citation Record · LEDGER

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models

As of 18 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2602.01027.

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

pith.paper-citation-record.v1
2602.01027 v2

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T05:51:31.515402Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

24 of 24 outbound references displayed

verified exact0
verified fuzzy0
unresolved20
parse uncertain0
malformed identifier4
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f99961bb-0201-4e3a-b6e7-71084d46ff01 · outbound

This paper cites This decomposition is performed offline, incurring no runtime overhead.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models This decomposition is performed offline, incurring no runtime overhead

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:31.066693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96d42862-c698-47ab-916e-fc9e0d24e688 · outbound

This paper cites FlexQuant: A flexible and effi- cient dynamic precision switching framework for LLM quantization.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models FlexQuant: A flexible and effi- cient dynamic precision switching framework for LLM quantization

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:30.390960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c07e462a-6dc3-4245-ac26-4920e9063e90 · outbound

This paper cites J., and Lee, D.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models J., and Lee, D

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:30.441751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79ef2b9a-3ca7-4377-a13d-2a2811e76cec · outbound

This paper cites LRP-QViT: Mixed-Precision Vision Transformer Quantization via Layer-wise Relevance Propagation.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models LRP-QViT: Mixed-Precision Vision Transformer Quantization via Layer-wise Relevance Propagation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:30.530975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c080e3a-e7b1-4c4c-b1a2-e41dee2f975a · outbound

This paper cites Mix-QViT: Mixed-Precision Vision Transformer Quantization Driven by Layer Importance and Quantization Sensitivity.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models Mix-QViT: Mixed-Precision Vision Transformer Quantization Driven by Layer Importance and Quantization Sensitivity

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:30.629706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b09a6f1a-ac4a-4b30-bbf3-4870a44f81fe · outbound

This paper cites OutlierTune: Efficient Channel-Wise Quantization for Large Language Models.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models OutlierTune: Efficient Channel-Wise Quantization for Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:30.723176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 371cd540-f097-4396-b6e0-45fd58509ee7 · outbound

This paper cites Additional Related Works A.1.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models Additional Related Works A.1

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:30.866891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a749092a-d018-46fe-bdcf-b7bc8319b8f7 · outbound

This paper cites 13 SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models C.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models 13 SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models C

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:31.120544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2dc53d0-a6c5-4b7b-801f-232c1b18e7c5 · outbound

This paper cites For SliM-LLM, we use the official released code, while GPTQ is evaluated using GPTQModel.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models For SliM-LLM, we use the official released code, while GPTQ is evaluated using GPTQModel

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:31.185369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa679456-d946-43df-b99c-48e5a011b44e · outbound

This paper cites an unresolved cited work.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models Unresolved cited work

Reference 24

Resolution: malformed identifier; no resolver link, observed 2026-08-03T05:51:31.515402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3468b0bb-a256-4d36-ba76-0d782269076b · outbound

This paper cites The numbers on the left indicate the BPW per configuration.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models The numbers on the left indicate the BPW per configuration

Reference 128

Resolution: malformed identifier; no resolver link, observed 2026-08-03T05:51:31.360193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c3e449b-1a75-4a89-80ae-c5a6aed7f933 · outbound

This paper cites an unresolved cited work.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models Unresolved cited work

Reference 256

Resolution: malformed identifier; no resolver link, observed 2026-08-03T05:51:31.414333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61100ab3-d2a0-4391-89c9-7b4d9f78fd43 · outbound

This paper cites Impact of Sample Size for Fisher Estimation Table 7 reports the impact of the sample size used for Fisher information estimation on model performance.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models Impact of Sample Size for Fisher Estimation Table 7 reports the impact of the sample size used for Fisher information estimation on model performance

Reference 512

Resolution: malformed identifier; no resolver link, observed 2026-08-03T05:51:31.289384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c05316d8-3281-4afe-b72f-00f51767be64 · outbound

This paper cites Amq: Enabling automl for mixed-precision weight-only quan- tization of large language models.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models Amq: Enabling automl for mixed-precision weight-only quan- tization of large language models

Reference 1983

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:30.297783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b67e4756-23bd-44ba-a7d9-efcd2a5e17aa · outbound

This paper cites FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference

Reference 1997

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:30.119869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6f6817e-1509-4d4a-bbec-487f10be856c · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models Training Verifiers to Solve Math Word Problems

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:29.913387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e020b0d6-fda5-45f6-a1a0-524e09821236 · outbound

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

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:29.818370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a885373-c414-4ce8-ac78-1d6a1097dd49 · outbound

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

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models EfficientQAT: Efficient Quantization-Aware Training for Large Language Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:29.522870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d07b3f5-3007-4ee6-b7cb-87c2dc8ac0e2 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models Measuring Massive Multitask Language Understanding

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:30.007463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6ce942e-894d-46d8-b40c-8b80fc05f889 · outbound

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

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models BoolQ: Exploring the surprising difficulty of natural yes/no questions

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:29.699920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26d03976-9015-4ceb-8156-8d7d14806483 · outbound

This paper cites Qwen3 Technical Report.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models Qwen3 Technical Report

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:30.814398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 499fa78d-58e5-48d5-99f9-ddd4718a8c85 · outbound

This paper cites SignRoundV2: Toward Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models SignRoundV2: Toward Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:29.598001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 703a82b9-2fee-4549-a5d1-79b90dae3a5a · outbound

This paper cites DILEMMA: Joint LLM Quantization and Distributed LLM Inference Over Edge Computing Systems.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models DILEMMA: Joint LLM Quantization and Distributed LLM Inference Over Edge Computing Systems

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:30.204694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abfc6463-d4ef-4539-8a44-ea3504bea22d · outbound

This paper cites Most approaches (Cheng et al., 2025; You et al.,.

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models Most approaches (Cheng et al., 2025; You et al.,

Reference 2560

Resolution: unresolved; no resolver link, observed 2026-08-03T05:51:30.979918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.