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ROAM introduces a relation-guided framework for managing atomic memories in AI agents, improving answer accuracy by up to 29.8 percentage points through semantic classification and memory fusion.
This paper explores how semantic relations are encoded in the geometry of language model semantic spaces, finding that asymmetric relations occupy distinct regions and that lexical information matters more for causal models while contextual information matters more for masked and diffusion models.