RefereeBench shows that even the strongest video MLLMs reach only around 60% accuracy on multi-sport refereeing tasks and struggle with rule application and temporal grounding.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
AdecPilot decentralizes administration in edge-cloud multi-agent frameworks by using a UI-agnostic cloud designer and a bimodal edge team with a Hierarchical Implicit Termination protocol, yielding 21.7% higher task success, 37.5% less cloud tokens, and 88.9% lower latency.
citing papers explorer
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RefereeBench: Are Video MLLMs Ready to be Multi-Sport Referees
RefereeBench shows that even the strongest video MLLMs reach only around 60% accuracy on multi-sport refereeing tasks and struggle with rule application and temporal grounding.
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Administrative Decentralization in Edge-Cloud Multi-Agent for Mobile Automation
AdecPilot decentralizes administration in edge-cloud multi-agent frameworks by using a UI-agnostic cloud designer and a bimodal edge team with a Hierarchical Implicit Termination protocol, yielding 21.7% higher task success, 37.5% less cloud tokens, and 88.9% lower latency.