Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.10290.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T05:57:51.191050Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T05:57:51.482552Z
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 5fabf948-7a62-46ae-b886-3219b0a7faec · inbound
Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance cites this paper.
Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases
Reference 53
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T05:57:51.489118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3f7057cf-ccb5-4b8f-83f2-16eb5a338e01 · inbound
Is Your NPU Ready for LLMs? Dissecting the Hidden Efficiency Bottlenecks in Mobile LLM Inference cites this paper.
Is Your NPU Ready for LLMs? Dissecting the Hidden Efficiency Bottlenecks in Mobile LLM Inference MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T12:14:57.342551Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.