Paper Citation Record · LEDGER

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM

As of 8 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2602.20191.

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

pith.paper-citation-record.v1
2602.20191 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T21:53:20.715200Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

13 of 13 outbound references displayed

verified exact0
verified fuzzy0
unresolved12
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 79cde160-0886-48e4-bcac-3135eb5ff04c · outbound

This paper cites TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision.

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T21:53:20.211127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d493c7c-a6a5-4df7-be60-42d327583dc7 · outbound

This paper cites and Patterson, D.

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM and Patterson, D

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T21:53:20.295417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f5d2d71-e563-4c41-87f1-0f1877eabcef · outbound

This paper cites J., and Lee, D.

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM J., and Lee, D

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T21:53:20.414752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ec1bf84-1639-49c3-a580-a3077dd4f018 · outbound

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

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T21:53:20.504754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b04d30d-02c0-4e1c-85ff-71bbe0265205 · outbound

This paper cites Overall Algorithm of MoBiQuant.

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM Overall Algorithm of MoBiQuant

Reference 12

Resolution: malformed identifier; no resolver link, observed 2026-08-02T21:53:20.614769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fad77491-c7aa-49b6-95d7-8cc9f00e1ec7 · outbound

This paper cites LET preserves the main linear path by transforming the input activation and compensating the linear weights so that the layer output remains equivalent.

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM LET preserves the main linear path by transforming the input activation and compensating the linear weights so that the layer output remains equivalent

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T21:53:20.715200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86a81e30-791e-4a1e-8881-bd623c7df1f5 · outbound

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

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-02T21:53:19.688454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc863dbc-6cf7-4162-8af6-298115b03b33 · outbound

This paper cites FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference.

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-02T21:53:19.986284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5730d99-596a-4f42-b972-8ba02549b4dd · outbound

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

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-02T21:53:19.794376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 141b2ca0-3e07-4514-926d-badf21f465f3 · outbound

This paper cites PrefixQuant: Eliminating Outliers by Prefixed Tokens for Large Language Models Quantization.

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM PrefixQuant: Eliminating Outliers by Prefixed Tokens for Large Language Models Quantization

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-02T21:53:19.546144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25bd78b3-a95f-442e-be59-9f3ed5ccdfc2 · outbound

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

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM A Survey of Quantization Methods for Efficient Neural Network Inference

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T21:53:19.873410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 358df1f3-b77a-4a79-a336-cc2c15041569 · outbound

This paper cites Mixtral of Experts.

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM Mixtral of Experts

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-02T21:53:20.106717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5668b894-29a7-45d9-a323-4b035854b1a3 · outbound

This paper cites Pointer Sentinel Mixture Models.

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM Pointer Sentinel Mixture Models

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-02T21:53:20.339214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.