Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2406.08155.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T04:25:48.982852Z
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
1
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 a78933dc-47ac-4271-b551-fc29047ec64d · inbound
A Survey on Inference Optimization Techniques for Mixture of Experts Models cites this paper.
A Survey on Inference Optimization Techniques for Mixture of Experts Models QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
Reference 98
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T12:44:35.729393Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f5ef0cf1-1054-4dfa-81f6-bc675d0600fe · inbound
Swarm Skills: A Portable, Self-Evolving Multi-Agent System Specification for Coordination Engineering cites this paper.
Swarm Skills: A Portable, Self-Evolving Multi-Agent System Specification for Coordination Engineering QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T07:36:59.129884Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 3cd4d4a6-2863-4583-8385-140bbca65611 · 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 QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
Reference 7
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-28T01:31:28.847292Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation fdf20858-6652-41a7-b4ad-f5e9836ccf75 · inbound
FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models cites this paper.
FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
Reference 9
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T20:03:56.504800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a8127f43-f456-4c49-bbe5-e48b994e8ed7 · inbound
On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domain cites this paper.
On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domain QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
Reference 12
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T21:18:58.224927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 963ba329-968b-451c-9c0a-517cb3b67327 · 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 QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T21:09:09.248989Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 41dbcc43-cdf7-41d7-8345-a8e43031b87e · inbound
Tied Trit-Planes: Constraining PTQTP to a Uniform Nine-Level Quantizer, with a Persistent Folded Format for Disk-Streamed Mixture-of-Experts Serving cites this paper.
Tied Trit-Planes: Constraining PTQTP to a Uniform Nine-Level Quantizer, with a Persistent Folded Format for Disk-Streamed Mixture-of-Experts Serving QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T04:25:48.982852Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.