Paper Citation Record · LEDGER

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation

As of 21 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2506.12038.

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

pith.paper-citation-record.v1
2506.12038 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:52:09.528561Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

15 of 15 outbound references displayed

verified exact0
verified fuzzy0
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1d868afb-fdfd-45fe-bb10-ff16c7170931 · outbound

This paper cites BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation.

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:08.203274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d36f68ad-7c89-4972-8b6d-d14ed1967ed3 · outbound

This paper cites LUT Tensor Core: A Software-Hardware Co-Design for LUT-Based Low-Bit LLM Inference.

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation LUT Tensor Core: A Software-Hardware Co-Design for LUT-Based Low-Bit LLM Inference

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:08.732203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5859a71c-6fe1-47ab-9d75-6a2fd0ab65bd · outbound

This paper cites QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving.

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:08.497513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c956017-46e5-40c7-a26f-c8f8c57bed18 · outbound

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

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:08.874485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55b22908-f6be-49f3-9697-51984118ec04 · outbound

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

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:08.979428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 653af550-633e-4c12-99bf-49198969a537 · outbound

This paper cites QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks.

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:09.141264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36afecd2-8419-46fb-96fe-196bf4db64f2 · outbound

This paper cites GPTVQ: The Blessing of Dimensionality for LLM Quantization.

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation GPTVQ: The Blessing of Dimensionality for LLM Quantization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:09.236149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 166bd058-5a54-4b87-b8ab-aa46a9546cc6 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:09.366432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15a25ab4-7072-4fc3-99ee-5c40b7d6a3ab · outbound

This paper cites T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge.

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:09.442301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50335a6d-261b-437f-9fce-7d0d6613c3f4 · outbound

This paper cites Pointer Sentinel Mixture Models.

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation Pointer Sentinel Mixture Models

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:08.658397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4f5fb73-8bc0-4067-a3ca-6404da8b5060 · outbound

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

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:08.049791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd3662d5-ae49-40de-8dc5-cc29dc48f693 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:09.528561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5651f642-c9cd-4521-bae5-ccdb8f8fa297 · outbound

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

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:08.362034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 090a4bb0-5d7c-4879-a42f-42dba738a588 · outbound

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

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:08.583287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcc83397-63b7-4828-b9d2-9a86a8d06783 · outbound

This paper cites SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization.

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization

Reference 2024

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T14:52:10.063397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.