8 papers · 1 filter
Infinitesimal Causality
Sridhar Mahadevan
Interventions can be varied continuously in many causal models. Differentiating a specified smooth intervention protocol produces vector fields on a statistical model, and their Li…
Latent Confounded Causal Discovery via Lie Bracket Geometry
Sridhar Mahadevan
We study causal discovery from observational and interventional regimes when latent variables may affect the measured system. Our first algorithm, BRIDGE (Bracket Residuals for Int…
Causal Density Functions
Sridhar Mahadevan
We study the full density ratio between a specified intervention regime and an observational regime , , under the prerequisite . We call the r…
Learning in Infinitesimal Non-Compositional Sketches
Sridhar Mahadevan
This paper develops a categorical framework -- Learning in Infinitesimal Non-Compositional Sketches (LINCS) -- as the repair of non-compositionality: failures of diagrams to factor…
Agentic Skill Optimization over Lie Algebroids
Sridhar Mahadevan
Agentic systems increasingly improve themselves by editing skills: prompts, rubrics, plans, tool contracts, examples, validators, and traces. Skill edits are not independent coordi…
Odyssey: Constructing Verifiable Local Truth-Preserving Foundation Models
Sridhar Mahadevan
We introduce a categorical framework called ODYSSEY for constructing verifiable, local truth-preserving foundation models as compositions of foundries: building-block architectural…
PROMETHEUS: Automating Deep Causal Research Integrating Text, Data and Models
Sridhar Mahadevan
Large language models can extract local causal claims from text, but those claims become more useful when organized as persistent, navigable world models rather than as flat summar…
Universal Decision Learners
Sridhar Mahadevan
Many theories of decision making -- planning, reinforcement learning, causal intervention, online learning, and game-theoretic equilibrium -- turn local information into globally c…