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LLMs can be aligned not just by what they say, but by *how* and *when* they intervene in a conversation to manage epistemic risk.
Geometric Algebra offers a principled algebraic framework that captures higher-order semantic interactions, potentially resolving persistent limitations in compositional semantics and interpretability that plague current linear algebra-based NLP models.
LLMs falter at tracking shared beliefs and task progression in a novel multimodal collaboration task, highlighting a gap in their ability to reason under epistemic asymmetry.
