Paper Citation Record · LEDGER

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models

As of 7 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2508.06974.

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

pith.paper-citation-record.v1
2508.06974 v2

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-21T23:44:01.953344Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

48 of 48 outbound references displayed

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verified fuzzy19
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d79a5b07-aa4d-4be3-af4f-5304cca06bb9 · outbound

This paper cites Smollm - blazingly fast and remarkably powerful.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Smollm - blazingly fast and remarkably powerful

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.993566Z

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Observation b2235490-13da-48b2-b174-0588ee5bad19 · outbound

This paper cites Pythia: A suite for analyzing large language models across training and scaling.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Pythia: A suite for analyzing large language models across training and scaling

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.991276Z

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Observation c47549a9-86aa-4048-9145-a8bdab7b8895 · outbound

This paper cites Piqa: Reasoning about physical common- sense in natural language.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Piqa: Reasoning about physical common- sense in natural language

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.989224Z

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Observation 43f53ec0-0e2c-48d6-b533-461b8a86d462 · outbound

This paper cites DB-LLM: Accurate Dual-Binarization for Efficient LLMs.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models DB-LLM: Accurate Dual-Binarization for Efficient LLMs

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.571115Z

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Observation 2fe7647d-e7c1-459b-abdf-91f830724b37 · outbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.608473Z

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Observation ae44b037-8c31-4651-a19f-79a74aab61df · outbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.576555Z

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Observation 826eb67d-47d2-4620-b6fa-2ac37683b083 · outbound

This paper cites Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.573928Z

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Observation d6f57e09-ca67-4fc3-9378-859482f219b3 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models QLoRA: Efficient Finetuning of Quantized LLMs

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.602093Z

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Observation a39cb12d-e94e-4a54-a86b-a51a6c8f777e · outbound

This paper cites CBQ: Cross-Block Quantization for Large Language Models.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models CBQ: Cross-Block Quantization for Large Language Models

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.521848Z

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Observation c0f8836b-99b0-4261-b933-60fab890d7bb · outbound

This paper cites The Llama 3 Herd of Models.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models The Llama 3 Herd of Models

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.544029Z

Source-reported events for the cited work

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Observation c2275535-b065-42b6-8fec-ecc2f5c5e1a7 · outbound

This paper cites lm-evaluation-harness.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models lm-evaluation-harness

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.965808Z

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Observation b939caed-17f6-458a-b178-6179a76e0f41 · outbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.494224Z

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Observation 1edfb4d1-21f7-49cf-a229-b2de744d9336 · outbound

This paper cites Pt-bitnet: 1-bit large language model with post-training quantization.Available at SSRN 4987078.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Pt-bitnet: 1-bit large language model with post-training quantization.Available at SSRN 4987078

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.963499Z

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Observation 11bbacbb-8d30-4d7a-ba15-e6e76bdae4f2 · outbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.527117Z

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Observation d6550fcd-d067-451e-a83f-53a038108174 · outbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.517127Z

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Observation 36ab9c8b-9f78-45f8-b24d-30d41faff821 · outbound

This paper cites Arb-llm: Alternating refined binarizations for large language models.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Arb-llm: Alternating refined binarizations for large language models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.612305Z

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Observation 35d6c0e9-7f00-4215-95bf-0cc5c01f1e78 · outbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.960926Z

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Observation f15300df-35d1-4eae-8119-7d1fd0d9cb86 · outbound

This paper cites Rotated binary neural network.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Rotated binary neural network

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:27.002649Z

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Observation 6bac20f3-ce57-498f-bc25-413a448e58bd · outbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.498527Z

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Observation 868cf11a-c3ea-48f2-989e-0e8669b356bb · outbound

This paper cites Reactnet: Towards precise binary neural network with generalized activation functions.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Reactnet: Towards precise binary neural network with generalized activation functions

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.970668Z

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Observation 228f981d-3990-4ee7-9114-36f5423c8cd1 · outbound

This paper cites FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.565633Z

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Observation bffe5d69-1676-4f0e-895e-14c712dc2cfc · outbound

This paper cites BitNet b1.58 2B4T Technical Report.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models BitNet b1.58 2B4T Technical Report

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.502882Z

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Observation 960f05ba-c912-4153-9768-d97c5c8f7c9f · outbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.580598Z

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Observation 1b8318a1-4a4d-42b2-8970-983d268da28e · outbound

This paper cites On the State of the Art of Evaluation in Neural Language Models.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models On the State of the Art of Evaluation in Neural Language Models

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.546870Z

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Observation 34475b4e-2d20-43c1-9d9f-8336b1a268b2 · outbound

This paper cites Pointer Sentinel Mixture Models.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Pointer Sentinel Mixture Models

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.540367Z

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Observation 544f4c9e-90aa-47d7-ac9d-7f6f7ad4eb1c · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.982644Z

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Observation a159ed2f-1016-4907-9ab5-1d344a82f632 · outbound

This paper cites Fine-tuning llms to 1.58bit: extreme quantization made easy.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Fine-tuning llms to 1.58bit: extreme quantization made easy

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.975498Z

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

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

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

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Observation 51ab51f2-d2e1-4fc5-a309-3251ba7a6d12 · outbound

This paper cites Forward and backward information retention for accurate binary neural networks.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Forward and backward information retention for accurate binary neural networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:27.000308Z

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Observation 8b65706a-f54e-4f0e-a50f-960953040056 · outbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.972869Z

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Observation 2c8352a2-9ead-44b3-bead-c8ee567ea53e · outbound

This paper cites Xnor-net: Imagenet classifi- cation using binary convolutional neural networks.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Xnor-net: Imagenet classifi- cation using binary convolutional neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.977953Z

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Observation 6ee9f74c-1bf1-4b7b-97a3-30d711128413 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Winogrande: An adversarial winograd schema challenge at scale

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.986968Z

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Observation 514f4a66-06a9-46f2-bf2a-2466d9e24357 · outbound

This paper cites PB-LLM: Partially Binarized Large Language Models.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models PB-LLM: Partially Binarized Large Language Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.507749Z

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Observation 9be89c9d-141f-4c33-b32c-e71eee771ca9 · outbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.536656Z

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Observation 66aa30a7-72a9-4c8d-bd1d-bdd1aa3a9284 · outbound

This paper cites A Survey on Transformer Compression.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models A Survey on Transformer Compression

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.532491Z

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Observation 93cf768f-c094-44a4-9ad2-898333d87781 · outbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.552688Z

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Observation 22ea2b49-afbb-4103-960a-6908f5013168 · outbound

This paper cites Adabin: Improving binary neural networks with adaptive binary sets.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Adabin: Improving binary neural networks with adaptive binary sets

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.980502Z

Source-reported events for the cited work

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Observation 705b42e2-a142-4ff5-994c-b1fc13a29a1e · outbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.512528Z

Source-reported events for the cited work

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Observation e8d4a238-444a-4897-981e-b963eab614df · outbound

This paper cites RedPajama: an Open Dataset for Training Large Language Models.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models RedPajama: an Open Dataset for Training Large Language Models

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.591858Z

Source-reported events for the cited work

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Observation 52b8bc82-1cd3-4085-8d39-e24b832a39b1 · outbound

This paper cites T-mac: Cpu renaissance via table lookup for low-bit llm deployment on edge.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models T-mac: Cpu renaissance via table lookup for low-bit llm deployment on edge

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.984654Z

Source-reported events for the cited work

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Observation 8d20a149-08bc-4ba9-af82-e33b9482b635 · outbound

This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Smoothquant: Accurate and efficient post-training quantization for large language models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.995933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d9ffc058-5768-4068-be87-316fefd80943 · outbound

This paper cites OneBit: Towards Extremely Low-bit Large Language Models.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models OneBit: Towards Extremely Low-bit Large Language Models

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.598786Z

Source-reported events for the cited work

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Observation 6bc37fab-5d7a-4190-bf51-678eb5f27b7f · outbound

This paper cites Qwen3 technical report.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Qwen3 technical report

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.997946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 50843b79-997d-4fbd-9b7c-58e42d60dda3 · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? In Annual Meeting of the Association for Computational Linguistics.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Hellaswag: Can a machine really finish your sentence? In Annual Meeting of the Association for Computational Linguistics

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:44:26.968164Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f5081846-3faf-4393-b567-c1e3976d1222 · outbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models OPT: Open Pre-trained Transformer Language Models

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.584036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 650141fc-a28e-40ae-b472-ed55bc33111f · outbound

This paper cites A Survey of Large Language Models.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models A Survey of Large Language Models

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.561993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 37e22f10-ed19-4565-a04a-6a80c8e66a8e · outbound

This paper cites An Empirical Study of Qwen3 Quantization.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models An Empirical Study of Qwen3 Quantization

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.587231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1130f740-eca8-49f6-9c88-899cec7966ca · outbound

This paper cites Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

Reference 48

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