Paper Citation Record · LEDGER

MiLo: Efficient Quantized MoE Inference with Mixture of Low-Rank Compensators

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2504.02658.

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

pith.paper-citation-record.v1
2504.02658 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:14:54.173096Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-04T21:51:07.134745Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0c05730d-81fb-490f-a9a6-ed0bb62fc5f9 · inbound

Utility-Driven Speculative Decoding for Mixture-of-Experts cites this paper.

Utility-Driven Speculative Decoding for Mixture-of-Experts MiLo: Efficient Quantized MoE Inference with Mixture of Low-Rank Compensators

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:54.173096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b501d29a-8ed3-4e44-a26b-7bb7a540ad66 · 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? MiLo: Efficient Quantized MoE Inference with Mixture of Low-Rank Compensators

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-04T21:51:07.211175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ee6e218f-ac0d-4a91-ad32-5c40fc7a4fff · 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 MiLo: Efficient Quantized MoE Inference with Mixture of Low-Rank Compensators

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T23:41:52.871652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f8995d4-2726-4940-81b2-99d0e1db8f45 · inbound

PagedWeight: Efficient MoE LLM Serving with Dynamic Quality-Aware Weight Quantization cites this paper.

PagedWeight: Efficient MoE LLM Serving with Dynamic Quality-Aware Weight Quantization MiLo: Efficient Quantized MoE Inference with Mixture of Low-Rank Compensators

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T21:09:08.209539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.