Yang, D. (CSE) – Inner Monologue: a Pathway to Human-Like Reasoning for Complex Tasks

A central goal on the path toward general AI is to build systems capable of deliberative reasoning before action. Such systems should inspect what they know, identify what they need, seek or construct useful information, and revise their reasoning through intermediate cognitive states. This dissertation studies this goal through the lens of Inner Monologue (IM), a mechanism that enables AI systems to coordinate internal components, acquire external information, and reason through structured intermediate states.

I will first introduce IM as a mechanism for internal coordination in static information systems, where multiple models collaborate within one AI system to solve reasoning tasks. I will then extend IM to dynamic information systems, where AI system is learned to retrieve external information. Finally, I will present how IM can move beyond verbal reasoning toward multimodal thinking, where generated visual states represent the system’s current understanding and support iterative refinement.

Together, this dissertation demonstrates the success and potential of human-inspired Inner Monologue mechanisms for improving complex multi-step reasoning in AI systems.

Event Host: Diji Yang, Ph.D. Candidate, Computer Science & Engineering

Advisor: Yi Zhang

Zoom: https://ucsc.zoom.us/j/99915235963?pwd=7Jqo6fc83LWobTEYRZCUzbrWbeov3Y.1

Passcode: 7Jqo6fc83LWobTEYRZCUzbrWbeov3Y.1