Paper Citation Record · LEDGER

Is (Selective) Round-To-Nearest Quantization All You Need?

As of 8 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2505.15909.

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

pith.paper-citation-record.v1
2505.15909 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:14:02.080691Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T17:16:25.669457Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T07:11:01.886740Z

Reference resolution

13 of 13 outbound references displayed

verified exact0
verified fuzzy4
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a6538a58-2ac6-4ec0-aced-d616f29c6d1a · outbound

This paper cites https://unsloth.ai/blog/ deepseekr1-dynamic.

Is (Selective) Round-To-Nearest Quantization All You Need? https://unsloth.ai/blog/ deepseekr1-dynamic

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:14:03.049093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9521a532-ddc7-4b18-b844-9b3a3a600efe · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

Is (Selective) Round-To-Nearest Quantization All You Need? Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T15:14:01.643150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6fd63f9-7b3e-405b-bc2d-3722c729f073 · outbound

This paper cites https://ai.meta.com/blog/meta- llama-3-1.

Is (Selective) Round-To-Nearest Quantization All You Need? https://ai.meta.com/blog/meta- llama-3-1

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:14:02.890161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ace44f33-2e20-45f7-8e08-e69af2e927c6 · outbound

This paper cites https://ai.meta.com/blog/llama- 4-multimodal-intelligence.

Is (Selective) Round-To-Nearest Quantization All You Need? https://ai.meta.com/blog/llama- 4-multimodal-intelligence

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:14:02.711764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7b44f1a9-9144-41ac-af05-7962c3b1a289 · outbound

This paper cites https://neuralmagic.com/blog/ introducing-machete-a-mixed-input-gemm- kernel-optimized-for-nvidia-hopper-gpus.

Is (Selective) Round-To-Nearest Quantization All You Need? https://neuralmagic.com/blog/ introducing-machete-a-mixed-input-gemm- kernel-optimized-for-nvidia-hopper-gpus

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:14:02.551437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 428558c0-df21-44d1-b1de-79ebf8a71218 · outbound

This paper cites QQQ: Quality Quattuor-Bit Quantization for Large Language Models.

Is (Selective) Round-To-Nearest Quantization All You Need? QQQ: Quality Quattuor-Bit Quantization for Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:14:02.080691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e87f1da-b40b-4b96-8c95-86ea70d5ea83 · outbound

This paper cites Pointer Sentinel Mixture Models.

Is (Selective) Round-To-Nearest Quantization All You Need? Pointer Sentinel Mixture Models

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T15:14:01.711931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16491b37-bd34-4fb7-bc8b-809da5b0812f · outbound

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

Is (Selective) Round-To-Nearest Quantization All You Need? Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T15:14:01.295431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 810a13ab-87b4-4e16-85a3-25780efd89ce · outbound

This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

Is (Selective) Round-To-Nearest Quantization All You Need? WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T15:14:01.857366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 769f1d7e-efac-45a7-9a6a-5e7e5b8f84a3 · outbound

This paper cites A Survey of Quantization Methods for Efficient Neural Network Inference.

Is (Selective) Round-To-Nearest Quantization All You Need? A Survey of Quantization Methods for Efficient Neural Network Inference

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T15:14:01.468760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e182f068-bc3e-4994-be2b-2d5fce860156 · outbound

This paper cites ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks.

Is (Selective) Round-To-Nearest Quantization All You Need? ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T15:14:02.036257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d38cf8b-ea40-411f-9334-1878b369d20b · outbound

This paper cites MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models.

Is (Selective) Round-To-Nearest Quantization All You Need? MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T15:14:01.423333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00f0ce72-798f-4ef3-8e0a-207169f3ba95 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Is (Selective) Round-To-Nearest Quantization All You Need? DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T15:14:01.342650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 6a81b4ac-6f01-460f-801f-6ca44b02ce3e · inbound

Weight Group-wise Post-Training Quantization for Medical Foundation Model cites this paper.

Weight Group-wise Post-Training Quantization for Medical Foundation Model Is (Selective) Round-To-Nearest Quantization All You Need?

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:11:01.893272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.