Paper Citation Record · LEDGER

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

As of 17 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 11 inbound Pith citation observations for arXiv:2411.17691.

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

pith.paper-citation-record.v1
2411.17691 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:54:14.306726Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:17:46.230253Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:19:44.242007Z

Reference resolution

25 of 25 outbound references displayed

verified exact1
verified fuzzy1
unresolved23
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d2c50ba8-8588-4b3e-86b2-f8408cb32a25 · outbound

This paper cites BinaryBERT: Pushing the Limit of BERT Quantization.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens BinaryBERT: Pushing the Limit of BERT Quantization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.189316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26811f73-94ab-462d-820a-9fff8ea58d6b · outbound

This paper cites How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.204402Z

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Observation bd937697-efff-4ac8-a4b5-6a9d304de3b0 · outbound

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

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.209560Z

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Observation b520850b-cb89-4ff1-a4ee-47b347a93e8a · outbound

This paper cites Scaling Synthetic Data Creation with 1,000,000,000 Personas.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens Scaling Synthetic Data Creation with 1,000,000,000 Personas

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.214834Z

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Unavailable: canonical work link unavailable.

Observation e56f4b0d-f196-439d-ae30-15e8f461ad2f · outbound

This paper cites Training Compute-Optimal Large Language Models.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens Training Compute-Optimal Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.219804Z

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Unavailable: canonical work link unavailable.

Observation e887f95c-5004-4f7e-a113-7ba33b48ea57 · outbound

This paper cites BiLLM: Pushing the Limit of Post-Training Quantization for LLMs.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.224148Z

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Unavailable: canonical work link unavailable.

Observation 17263cce-74ea-4241-bec0-a44337bbbf42 · outbound

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

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.238942Z

Source-reported events for the cited work

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Observation efedcb71-c729-4166-8787-d56087dc302c · outbound

This paper cites Scaling Laws for Precision.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens Scaling Laws for Precision

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.243786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 347f5051-1e66-4324-b609-a04ad358b2ba · outbound

This paper cites QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.248317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfb153fb-ef7e-4279-a742-ce914a792fe8 · outbound

This paper cites The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.257843Z

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Unavailable: canonical work link unavailable.

Observation a305711d-5bf4-4601-86c5-27ca415d59af · outbound

This paper cites Pointer Sentinel Mixture Models.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens Pointer Sentinel Mixture Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.262826Z

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Unavailable: canonical work link unavailable.

Observation b4927941-ad7d-4a2b-bc2e-3c4715f05196 · outbound

This paper cites Opening the Black Box of Deep Neural Networks via Information.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens Opening the Black Box of Deep Neural Networks via Information

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.272871Z

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Unavailable: canonical work link unavailable.

Observation 2e039032-d0b2-4cbc-ba98-121f4a1ab555 · outbound

This paper cites BitNet: Scaling 1-bit Transformers for Large Language Models.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.281922Z

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Unavailable: canonical work link unavailable.

Observation 3ac6968d-610d-46eb-840f-40632a7b9177 · outbound

This paper cites Qwen2 Technical Report.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens Qwen2 Technical Report

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.287292Z

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Unavailable: canonical work link unavailable.

Observation 38f46bee-5ba7-4a7f-9005-042dcba365d2 · outbound

This paper cites Q8bert: Quantized 8bit bert.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens Q8bert: Quantized 8bit bert

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:54:14.693576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e2cae76a-1130-44cf-9f76-594bc2154718 · outbound

This paper cites TernaryBERT: Distillation-aware Ultra-low Bit BERT.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens TernaryBERT: Distillation-aware Ultra-low Bit BERT

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.302231Z

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Unavailable: canonical work link unavailable.

Observation 78419917-98e9-4c69-9c2f-d0b5af339a56 · outbound

This paper cites Towards Accurate Post-Training Quantization of Vision Transformers via Error Reduction.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens Towards Accurate Post-Training Quantization of Vision Transformers via Error Reduction

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-12T11:54:14.349391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9ca519cc-2774-4cb6-b5e7-536727ed478d · outbound

This paper cites The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.268075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec6509c5-1df7-4e7b-818c-a7b2315f4a49 · outbound

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

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.277117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4ccee49-983b-4ed5-a505-691e89e1be7e · outbound

This paper cites Scaling Laws for Neural Language Models.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens Scaling Laws for Neural Language Models

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.229700Z

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Observation 9565334e-9db9-417e-b9b0-4cfe4c90b3b9 · outbound

This paper cites LQER: Low-Rank Quantization Error Reconstruction for LLMs.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens LQER: Low-Rank Quantization Error Reconstruction for LLMs

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.297560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7282a917-e857-4a2b-b1dd-a5c4977919df · outbound

This paper cites Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.234235Z

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Unavailable: canonical work link unavailable.

Observation e4f3d7d4-439e-418f-a8da-b0bea9ba7502 · outbound

This paper cites The Llama 3 Herd of Models.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens The Llama 3 Herd of Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.195117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02eea9cf-9ef5-4404-af06-5dfab48cc3c3 · outbound

This paper cites KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.253467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f917d45c-583b-4616-a364-4c771f1052cb · outbound

This paper cites Extreme Compression of Large Language Models via Additive Quantization.

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens Extreme Compression of Large Language Models via Additive Quantization

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T11:54:14.199479Z

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Pith citing papers

Observation 6c252e7d-47d1-4d0f-9ca1-9490a2a02c51 · inbound

Scaling Law for Quantization-Aware Training cites this paper.

Scaling Law for Quantization-Aware Training Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T15:41:08.080609Z

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Observation f2ea6853-3d3e-4e80-bfc8-c247008de273 · inbound

Characterization and Mitigation of Training Instabilities in Microscaling Formats cites this paper.

Characterization and Mitigation of Training Instabilities in Microscaling Formats Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T22:47:52.264775Z

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Unavailable: canonical work link unavailable.

Observation 7fddf69d-67b1-4f3b-b97f-3c33b387aaf9 · inbound

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models cites this paper.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.595598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8927b930-967d-4780-bf3a-ff9f647b04da · inbound

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation cites this paper.

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

Reference 100

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:46:04.726341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 634a03fe-9213-4221-ace5-09bea61f201f · inbound

Hyperloop Transformers cites this paper.

Hyperloop Transformers Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:16:04.399141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 38216f5d-312e-456c-bafe-110d40f0bde7 · inbound

BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment cites this paper.

BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:46:43.044744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e24e0882-ef18-4a92-af51-d1b21124b206 · inbound

FTerViT: Fully Ternary Vision Transformer cites this paper.

FTerViT: Fully Ternary Vision Transformer Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:03:59.476277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dbd5c2c7-0cad-45fd-b40b-7b151f9c0f79 · inbound

LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws cites this paper.

LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-25T04:35:21.786530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 15fb61d1-308b-4cc1-aa01-cebb11a8ea88 · inbound

On the Expressive Power of Weight Quantization in Large Language Models cites this paper.

On the Expressive Power of Weight Quantization in Large Language Models Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:19:44.243529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7005b6a1-ea7b-48ef-b0fa-8a5d36162a49 · inbound

Which Decisions Low-Bit Quantization Breaks, and How to Predict Them cites this paper.

Which Decisions Low-Bit Quantization Breaks, and How to Predict Them Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T04:22:45.225224Z

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Unavailable: canonical work link unavailable.

Observation b5a492f1-8694-46b4-b804-94a7098d8202 · inbound

Which Decisions Low-Bit Quantization Breaks, and How to Predict Them cites this paper.

Which Decisions Low-Bit Quantization Breaks, and How to Predict Them Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T04:17:46.230253Z

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Unavailable: canonical work link unavailable.