Three frameworks adapt foundation models for generalized category discovery under domain shifts via disentanglement and prompt tuning, showing gains on synthetic and real multi-domain data.
A stochastic approximation method
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DAOEF integrates delta-aware caching, action pruning, and hardware matching to deliver 1.45x gains and sub-linear scaling up to 250 agents in multi-agent edge computing.
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Generalized Category Discovery under Domain Shifts: From Vision to Vision-Language Models
Three frameworks adapt foundation models for generalized category discovery under domain shifts via disentanglement and prompt tuning, showing gains on synthetic and real multi-domain data.
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A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing
DAOEF integrates delta-aware caching, action pruning, and hardware matching to deliver 1.45x gains and sub-linear scaling up to 250 agents in multi-agent edge computing.