Publications
Managing the Stochastic: Foundations of Learning in Neuro-Symbolic Systems for Software Engineering Matthew Thompson (2025)
arXiv:2512.20660 | Code
Abstract: Current approaches to AI coding agents appear to blur the lines between the Large Language Model (LLM) and the agent itself, asking the LLM to make decisions best left to deterministic processes. This paper proposes setting the control boundary such that the LLM is treated as a component of the environment—preserving its creative stochasticity—rather than the decision-making agent. A Dual-State Architecture is formalized, separating workflow state (deterministic control flow) from environment state (stochastic generation).
Foundational Series
Deep-dives into the theory behind autonomous agents.
The Intelligent Agents Series A formal exploration of agent architectures, search algorithms, and rationality.
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Reinforcement Learning & Q-Learning Implementations and analysis of Q-learning, Bayesian games, and decision processes.
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