COMPASS uses VLMs to generate and refine code-based strategies with structured communication, achieving 57% win rate on SMACv2 Protoss 5v5 versus 27% for QMIX.
Building llm-based AI agents in social virtual reality
2 Pith papers cite this work, alongside 75 external citations. Polarity classification is still indexing.
2
Pith papers citing it
75
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years
2025 2verdicts
UNVERDICTED 2representative citing papers
AIvaluateXR benchmarks 17 LLMs across four XR platforms on performance, speed, memory and battery metrics and proposes a 3D Pareto optimality method to identify optimal on-device model-device pairs.
citing papers explorer
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Closed-Loop Vision-Language Planning for Multi-Agent Coordination
COMPASS uses VLMs to generate and refine code-based strategies with structured communication, achieving 57% win rate on SMACv2 Protoss 5v5 versus 27% for QMIX.
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AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results
AIvaluateXR benchmarks 17 LLMs across four XR platforms on performance, speed, memory and battery metrics and proposes a 3D Pareto optimality method to identify optimal on-device model-device pairs.