Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.00425.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:09:10.222780Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T05:56:57.204200Z
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 d3466021-9e02-4e90-a8f3-43e92a6c9323 · inbound
EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models cites this paper.
EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization
Reference 68
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:09:10.222780Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0a64c309-4bed-4ea1-9647-9e54130324de · inbound
Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques cites this paper.
Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization
Reference 88
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T05:56:57.208416Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.