Artificial Intelligence

2605 Submissions

[2] viXra:2605.0052 [pdf] submitted on 2026-05-13 19:21:40

Probabilistic Performance Profiling for Non-Deterministic Agentic AI Systems

Authors: Raghavendra Venkateshappa
Comments: 16 Pages. (Note by viXra Admin: Please submit article written with AI assistance to ai.viXra.org)

Non-deterministic agentic AI systems present fundamental challenges for traditional performance testing methodologies that rely on deterministic metrics and reproducible measurements. We propose a novel probabilistic performance profiling framework that models agent performance as probability distributions rather than point estimates. Our approach leverages Monte Carlo sampling to generate comprehensive performance distribution profiles across diverse execution contexts, while employing Bayesian inference for continuous model refinement based on observed system behavior. The framework provides confidence intervals, performance bounds, and probabilistic guarantees that enable robust decision-making under uncertainty. Extensive evaluation on multiple agent frameworks demonstrates that our approach captures performance variability more accurately than traditional methods, providing 95% confidence intervals with mean absolute errors below 8% across different task complexities. This work establishes the foundational framework for probabilistic performance assessment in agentic systems, enabling more reliable deployment and monitoring of non-deterministic AI agents.
Category: Artificial Intelligence

[1] viXra:2605.0027 [pdf] submitted on 2026-05-09 13:34:51

A Simple 2 X 2 Neural Network with Linear Algebra, by Hand

Authors: Han de Bruijn
Comments: 5 Pages.

An extremely simple single-layer feedforward 2 x 2 neural network is the subject of this article. Because I feel it is important to understand some essential features of neural networks without the help of a computer. The network at hand can be completely described, mathematically, by elementary linear algebra. A working example with two inputs and one output is leading to the general case. A counter example with two outputs instead of one is presented as well. It is concluded that the network with one output has learning capability and the network with two outputs has not. The behaviour of the first network can be formulated in geometric terms: all points on a straight line through two given points in the input plane give the desired output. There are no other inputs that do the job. The network with two outputs, on the contrary, is not able to make any generalization. It does not learn from experience, so to speak. It's kind of surprising that the more intelligent network is characterized by a singular matrix, and the dumber network by a regular matrix of weights.
Category: Artificial Intelligence