From the 2 of 5 linked papers with an AI index.
2 citations · 2 across the 5 of their papers we have counts for
5 papers
A cubical formalisation of topos causal models: intervention, forcing, and a contextuality obstruction
Karen Sargsyan
Topos causal models recast causal inference inside a topos: a causal world is a presheaf, an intervention is a sub-model named by a characteristic map into the subobject classifier…
A cubical formalisation of conditional independence, Bayesian conditioning, and Pearl's d-separation soundness
Karen Sargsyan
The paper formalises conditional independence, Bayesian conditioning, and Pearl's d‑separation within Cubical Agda, showing that the usual convex‑algebra axiom is insufficient and…
Localizing Preference Aggregation Conflicts: A Graph-Theoretic Approach Using Sheaves
Karen Sargsyan
The paper presents a graph‑theoretic framework using discrete sheaves to identify and locate inconsistencies in aggregated preferences and partial rankings, staying ordinal rather…
Structural Enforcement of Statistical Rigor in AI-Driven Discovery: A Functional Architecture
Karen Sargsyan
AI-Scientist systems risk manufacturing spurious discoveries through uncontrolled multiple testing. We present a functional architecture that enforces statistical rigor at two leve…
Functorial Neural Architectures from Higher Inductive Types
Karen Sargsyan
Neural networks often learn the parts of a task but fail on novel combinations of those parts. We argue that this failure is architectural: a decoder generalizes compositionally on…