Paper Citation Record · LEDGER

MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2505.03804.

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

pith.paper-citation-record.v1
2505.03804 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T21:51:04.339431Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c0dca862-5775-4453-8c67-54634e5cf498 · inbound

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model? cites this paper.

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model? MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T21:51:04.339431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a063dfd-ee8a-45a6-869c-13db3188df59 · inbound

PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference cites this paper.

PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T23:41:52.868779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3bceaf8-4ca8-4521-ab13-a7ef3799b873 · inbound

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling cites this paper.

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:11:20.904553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2b31aa35-bdf1-4787-a728-e9362d439d05 · inbound

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling cites this paper.

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-19T18:02:42.225016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 50c9523e-c994-408d-9796-7985c3ff7d82 · inbound

Amortized-Precision Quantization for Early-Exit Vision Transformers cites this paper.

Amortized-Precision Quantization for Early-Exit Vision Transformers MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:40:52.175251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d3452b8e-70d2-434d-b80f-64f043fffd61 · inbound

Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models cites this paper.

Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:23:03.616046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 37b9ee1f-bb1e-4595-93d9-4d4164c544d8 · inbound

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs cites this paper.

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:36:39.891051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d483f08d-5f88-4a15-804e-a0720eec1406 · inbound

AlphaQ: Calibration-Free Bit Allocation for Mixture-of-Experts Quantization cites this paper.

AlphaQ: Calibration-Free Bit Allocation for Mixture-of-Experts Quantization MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:06:44.743847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ba76e5e7-e125-4f63-a8de-437dd7cd8b23 · inbound

Value-and-Structure Alignment for Routing-Consistent Quantization of Mixture-of-Experts Models cites this paper.

Value-and-Structure Alignment for Routing-Consistent Quantization of Mixture-of-Experts Models MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T01:31:28.846184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.