Veoautodes

Veoautodes is a term used in speculative artificial intelligence literature to denote self-generated, visual representations of an autonomous agent's internal states and prospective outcomes. A veoaudose typically combines sensory inputs, internal model variables, and projected goals into a compact, interpretable descriptor that the agent can reference during decision-making.

Origin and scope: The term was coined in 2023 by researchers exploring self-modeling and explainability in AI, and has since appeared in thought experiments and fictional case studies about transparent autonomous systems.

Concept and mechanisms: A veoaudose is produced by a self-referential module that maps current observations to a latent space and generates future-descriptive states. It functions as a self-forecast that supports action selection and can be used as a basis for explanation. The approach emphasizes modularity, where the veoaudose is separate from raw perception to preserve interpretability.

Applications and status: In fictional contexts, veoautodes are proposed as aids for debugging, human–machine collaboration, and alignment assessment, allowing observers to inspect the agent's intended trajectory without exposing raw policy details.

Criticism and limitations: Critics argue that veoautodes may oversimplify complex decision processes, introduce overreliance on interpretable summaries, and incur computational costs. There is no established standard definition, evaluation methodology, or consensus on reliability.

See also: Related concepts include self-modeling, introspective AI, and explainable AI.