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This paper presents a formal framework for constructing canonical interpretations from plural structure theories, motivated by structural failures in LLM-assisted reasoning. It distinguishes types of non-determinism and provides conditions for licensed canonicalization, without establishing full determinization for all cases.
This paper presents a formal framework for agentic business process analysis using the AGO methodology, defining agents, goals, and objects with formal precision to enable verifiable knowledge bases for agentic AI.
QUIVER introduces a formal framework for quantifying how perturbations propagate through compound AI systems structured as computation graphs, defining sensitivity matrices, trajectory divergence, bifurcation thresholds, and distribution faithfulness, with validation on production and public pipelines.
This extended paper revisits Semantic Web Services insights for Knowledge Graphs, proposing a four-dimensional formal framework and an Agentic Affordance Profile (AAP) to enable principled KG selection, composition, and failure diagnosis at agent planning time.