Paper Citation Record · LEDGER

BAQ: Efficient Bit Allocation Quantization for Large Language Models

As of 19 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 3 inbound Pith citation observations for arXiv:2506.05664.

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

pith.paper-citation-record.v1
2506.05664 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:18:55.423637Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-05T05:41:13.869451Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-05T05:50:43.590536Z

Reference resolution

28 of 28 outbound references displayed

verified exact0
verified fuzzy10
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d542fac4-a47a-4dfa-93b8-fb721f8ba1f6 · outbound

This paper cites Introducing ChatGPT.OpenAI Blog.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Introducing ChatGPT.OpenAI Blog

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:58.981600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 01ae5068-8aa3-42a2-bb3f-e8dc07648b74 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.Advances in neural information processing systems, 36:10088– 10115, 2023.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Qlora: Efficient finetuning of quantized llms.Advances in neural information processing systems, 36:10088– 10115, 2023

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:51.215053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d37587dc-eb2b-445b-8843-73e5bf796cd0 · outbound

This paper cites OPTQ: Accurate quantization for generative pre-trained transformers.

BAQ: Efficient Bit Allocation Quantization for Large Language Models OPTQ: Accurate quantization for generative pre-trained transformers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:51.356767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4638107-943c-4efa-842d-ece7443e4e08 · outbound

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

BAQ: Efficient Bit Allocation Quantization for Large Language Models BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:51.557589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adbbb647-40a9-4e9f-b99c-bc4e58e2d344 · outbound

This paper cites QuIP: 2-bit quantization of large language models with guarantees.

BAQ: Efficient Bit Allocation Quantization for Large Language Models QuIP: 2-bit quantization of large language models with guarantees

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:58.643512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 56f249c7-4c10-407a-bcfc-4a6227bbbff0 · outbound

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

BAQ: Efficient Bit Allocation Quantization for Large Language Models Smoothquant: Accurate and efficient post-training quantization for large language models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:51.833564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 825ef0da-5999-4b78-9e0e-5247cf497052 · outbound

This paper cites Omniquant: Omnidirectionally calibrated quan- tization for large language models.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Omniquant: Omnidirectionally calibrated quan- tization for large language models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:52.035060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 991ba312-d4ad-4764-8e7e-2ebcca51e8a8 · outbound

This paper cites Kvquant: Towards 10 million context length llm inference with kv cache quantization.Advances in Neural Information Processing Systems, 37:1270–1303, 2024.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Kvquant: Towards 10 million context length llm inference with kv cache quantization.Advances in Neural Information Processing Systems, 37:1270–1303, 2024

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:52.178887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f28f6709-fb4c-4d74-b87c-bdbdfe264093 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.Proceedings of Machine Learning and Systems, 6:87–100, 2024.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Awq: Activation-aware weight quantization for on-device llm compression and acceleration.Proceedings of Machine Learning and Systems, 6:87–100, 2024

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:52.315397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c08e9e6a-75e3-4440-a082-d8b03450aba9 · outbound

This paper cites Owq: Outlier- aware weight quantization for efficient fine-tuning and inference of large language models.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Owq: Outlier- aware weight quantization for efficient fine-tuning and inference of large language models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:52.490197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35aa94be-e2a4-4b81-aa83-0b824d01744a · outbound

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

BAQ: Efficient Bit Allocation Quantization for Large Language Models SqueezeLLM: Dense-and-Sparse Quantization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:52.624716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b7e28c6-9be5-4042-b20e-ccb2a8fb9961 · outbound

This paper cites Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan Alistarh.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan Alistarh

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:52.749496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52fbcbee-8dc5-48c3-b17d-615644050114 · outbound

This paper cites Optimal brain surgeon and general network pruning.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Optimal brain surgeon and general network pruning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:58.376113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c011a7f5-878c-40ab-836e-7053d7aa0c89 · outbound

This paper cites Optimal brain damage.Advances in neural information processing systems, 2, 1989.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Optimal brain damage.Advances in neural information processing systems, 2, 1989

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:53.069213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e00c5e90-9f8a-4caf-8480-2c0e09f4f89c · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:58.061585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 20cf8c7a-20cf-4c73-a47d-3e33a7b449c2 · outbound

This paper cites Optimal brain compression: A framework for accurate post- training quantization and pruning.Advances in Neural Information Processing Systems, 35:4475– 4488, 2022.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Optimal brain compression: A framework for accurate post- training quantization and pruning.Advances in Neural Information Processing Systems, 35:4475– 4488, 2022

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:53.356799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e388151-715e-4228-b3cf-11399f60e860 · outbound

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

BAQ: Efficient Bit Allocation Quantization for Large Language Models BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:53.534133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 767b367f-c3e5-41fd-9517-9335576483a1 · outbound

This paper cites Gray and David L.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Gray and David L

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:57.715317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a549fd08-e043-4463-a65f-5907fc04c75e · outbound

This paper cites John Wiley & Sons, 1999.

BAQ: Efficient Bit Allocation Quantization for Large Language Models John Wiley & Sons, 1999

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:53.878901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8d1f918-2e0c-40cb-aefc-0f4f22b8207d · outbound

This paper cites Jpeg2000: Image compression fundamentals, standards and practice.Journal of Electronic Imaging, 11(2):286–287, 2002.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Jpeg2000: Image compression fundamentals, standards and practice.Journal of Electronic Imaging, 11(2):286–287, 2002

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:57.352361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f486d404-4f12-4790-b780-fea2a955d48d · outbound

This paper cites Springer Science & Business Media, 2012.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Springer Science & Business Media, 2012

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:54.104520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dde2d55b-4767-4ea0-bb7d-46e6631a94c3 · outbound

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

BAQ: Efficient Bit Allocation Quantization for Large Language Models OPT: Open Pre-trained Transformer Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:54.262302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc1f0635-fd71-45ba-9a48-a4c83394244d · outbound

This paper cites an unresolved cited work.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Unresolved cited work

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:54.443826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 403ab597-f344-4cff-9ee5-8511a7f80c6f · outbound

This paper cites Pointer Sentinel Mixture Models.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Pointer Sentinel Mixture Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:54.621632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34702f25-e960-4259-8b8a-e4a69673bc55 · outbound

This paper cites The Penn Treebank: Annotating predicate argument structure.

BAQ: Efficient Bit Allocation Quantization for Large Language Models The Penn Treebank: Annotating predicate argument structure

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:57.082435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8e0adfe8-8572-40f5-a731-c30a19440f50 · outbound

This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context.

BAQ: Efficient Bit Allocation Quantization for Large Language Models The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:56.608156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3d4a6eec-b3dc-41ef-84eb-9abcfb0959b8 · outbound

This paper cites Piqa: An algebra for querying protein data sets.

BAQ: Efficient Bit Allocation Quantization for Large Language Models Piqa: An algebra for querying protein data sets

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:56.247548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e378cbbd-1a98-4b0f-9598-387bd629d825 · outbound

This paper cites A systematic classification of knowl- edge, reasoning, and context within the ARC dataset.

BAQ: Efficient Bit Allocation Quantization for Large Language Models A systematic classification of knowl- edge, reasoning, and context within the ARC dataset

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:55.902652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 196ce1c7-1a55-4e9f-98e8-714d0a7fa3c2 · inbound

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

SignRoundV2: Toward Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs BAQ: Efficient Bit Allocation Quantization for Large Language Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-21T17:10:24.851205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation db559af4-13dd-4bac-9c2a-f077de7e7051 · inbound

RateQuant: Optimal Mixed-Precision KV Cache Quantization via Rate-Distortion Theory cites this paper.

RateQuant: Optimal Mixed-Precision KV Cache Quantization via Rate-Distortion Theory BAQ: Efficient Bit Allocation Quantization for Large Language Models

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T04:40:59.669846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6267c1b7-0ebd-46ce-94bf-35493f06b28f · inbound

RateQuant: Optimal Mixed-Precision KV Cache Quantization via Rate-Distortion Theory cites this paper.

RateQuant: Optimal Mixed-Precision KV Cache Quantization via Rate-Distortion Theory BAQ: Efficient Bit Allocation Quantization for Large Language Models

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-07-05T05:50:43.592486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.