Hierarchical Compositionality for An Assistive AI Agent

AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are considered to be state of the art for such agents. These methods are impressive stochastic predictors, but they are resource-hungry, opaque, and known to make arbitrary decisions in novel situations due to the narrow set of underlying representation and processing choices. Our work seeks to explore the design of architectures for such AI agents b

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ethics.ai (11 August 2026), “Hierarchical Compositionality for An Assistive AI Agent,” evidence record 18433, https://ethics.ai/record/18433 (originally published by arXiv).

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