DISSERTATION DEFENSE

Author : Yuxin Zi
Advisors: Dr. Amit Sheth
Date: October 20, 2026
Time: 09:00 am
Location: AI Seminar Room 529
Link:  https://sc-edu.zoom.us/j/84342587325
Meeting ID: 843 4258 7325

Abstract

While Large Language Models (LLMs) possess a remarkable capacity to generate fluent, step-by-step explanations, their underlying reasoning processes remain epistemologically fragile. The prevailing paradigm of Chain-of-Thought (CoT) prompting forces formal, multi-dimensional logic into a one-dimensional textual sequence. This fundamental misalignment between the topology of deductive reasoning and the linearity of autoregressive generation conceals causal dependencies, leading to hallucination snowballs, context dilution, and catastrophic error cascading. Furthermore, current oversight mechanisms, such as linear Process Reward Models (PRMs), evaluate reasoning steps based on chronologically flat histories and symmetric distance metrics, thereby conflating mere topical relevance with rigorous logical entailment.

This dissertation introduces a paradigm shift in how artificial reasoning is represented, verified, grounded, and repaired. It argues that robust reasoning requires the explicit scaffolding of a machine-checkable Directed Acyclic Graph (DAG), rigorously governed by asymmetric geometry, neuro-symbolic boundaries, and continuous latent-space optimization.