8 papers
Agentic AI Systems and Financial Stability, From Model Risk to Systemic Risk
Sriram Nagaraj, Seung Jung Lee
Financial stability rests on the premise that distress is largely idiosyncratic and therefore diversifiable: when one institution errs, the rest of the system absorbs the shock. Th…
Telemetry and Concealment in Self-Adapting Generative AI: Logging Architecture, Adversarial Model Hiding, and the Limits of Detection
Sriram Nagaraj
Model risk management (MRM) guidance assumes a static model lifecycle, in which models are developed, independently validated, and implemented without further autonomous modificati…
Joint Lyapunov Certificates for K-Agent Generative AI Governance: Stochastic Stability, Emergent Ensemble Risk, and Zero-Knowledge Governance Attestation
Sriram Nagaraj
We develop a rigorous mathematical framework for the governance of systems of K self-adapting generative AI models under the principles of Model Risk Management (MRM). When multipl…
Recursive Governance: A Graph-Theoretic Framework for Risk Propagation and Drift Detection in Agentic AI Systems
Sriram Nagaraj, Advaith Nila Narayanan
As financial institutions transition from traditional predictive models to autonomous agentic systems, the static model inventory requirements of traditional model risk management…
Nonlocal Neural Tangent Kernels via Parameter-Space Interactions
Sriram Nagaraj, Vishakh Hari
The Neural Tangent Kernel (NTK) framework has provided deep insights into the training dynamics of neural networks under gradient flow. However, it relies on the assumption that th…
Marrying Compressed Sensing and Deep Signal Separation
Truman Hickok, Sriram Nagaraj
Blind signal separation (BSS) is an important and challenging signal processing task. Given an observed signal which is a superposition of a collection of unknown (hidden/latent) s…
Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines
Sriram Nagaraj, Truman Hickok
This paper is aimed at using the newly developing field of physics informed machine learning (PIML) to develop models for predicting the remaining useful lifetime (RUL) aircraft en…
BrowNNe: Brownian Nonlocal Neurons & Activation Functions
Sriram Nagaraj, Truman Hickok
It is generally thought that the use of stochastic activation functions in deep learning architectures yield models with superior generalization abilities. However, a sufficiently…