Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Toward Inference-optimal Mixture-of-Expert Large Language Models
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2404.02852.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T04:39:14.129153Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T22:17:25.648466Z
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 ef6d182c-d7c7-49aa-bfb8-18377aa8c010 · inbound
Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection cites this paper.
Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection Toward Inference-optimal Mixture-of-Expert Large Language Models
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T17:05:42.977995Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 40428787-b2ee-440b-9530-3fcf9cf18a3c · inbound
Densing Law of LLMs cites this paper.
Densing Law of LLMs Toward Inference-optimal Mixture-of-Expert Large Language Models
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T21:40:05.611932Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e290c4bd-76cd-4b00-990c-532da986a089 · inbound
Superposition in Transformers: A Novel Way of Building Mixture of Experts cites this paper.
Superposition in Transformers: A Novel Way of Building Mixture of Experts Toward Inference-optimal Mixture-of-Expert Large Language Models
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:53:58.523522Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a8ab3399-1928-4a3f-b2ff-da066e72d92b · inbound
Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models cites this paper.
Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models Toward Inference-optimal Mixture-of-Expert Large Language Models
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:19:28.988042Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7484afa8-63ab-4eb9-88ab-5d1e5d5b65e5 · inbound
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient cites this paper.
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient Toward Inference-optimal Mixture-of-Expert Large Language Models
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T20:06:23.601404Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b37768b1-fa5d-4e10-8b45-b1895e43fd9a · inbound
Position: Enough of Scaling LLMs! Lets Focus on Downscaling cites this paper.
Position: Enough of Scaling LLMs! Lets Focus on Downscaling Toward Inference-optimal Mixture-of-Expert Large Language Models
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T04:39:14.129153Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f8f91383-0e61-458f-851a-3e6491fc3c03 · inbound
The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts cites this paper.
The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts Toward Inference-optimal Mixture-of-Expert Large Language Models
Reference 1937
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:46:44.611831Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0a266c5c-26b2-4de4-9305-8f62cba40d3a · inbound
Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs cites this paper.
Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs Toward Inference-optimal Mixture-of-Expert Large Language Models
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:16:27.611557Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77d2ee34-e0c7-4ac3-a8e0-dcac3d8ed2bb · inbound
HELLoRA: Hot Experts Layer-Level Low-Rank Adaptation for Mixture-of-Experts Models cites this paper.
HELLoRA: Hot Experts Layer-Level Low-Rank Adaptation for Mixture-of-Experts Models Toward Inference-optimal Mixture-of-Expert Large Language Models
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T23:29:12.663126Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 94abea09-d897-4ab2-8068-35b4d514d5c2 · inbound
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale cites this paper.
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale Toward Inference-optimal Mixture-of-Expert Large Language Models
Reference 80
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T22:17:25.649806Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.