From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery

PhD Qualifying Examination


Title: "From Automation to Autonomy: A Survey on Large Language Models in
Scientific Discovery"

by

Mr. Tianshi ZHENG


Abstract:

Large Language Models (LLMs) are catalyzing a paradigm shift in scientific
discovery, evolving from task-specific automation tools into increasingly
autonomous agents and fundamentally redefining research processes and
human-AI collaboration. This survey systematically charts this burgeoning
field, placing a central focus on the changing roles and escalating
capabilities of LLMs in science. Through the lens of the scientific method,
we introduce a foundational three-level taxonomy—Tool, Analyst, and
Scientist—to delineate their escalating autonomy and evolving
responsibilities within the research lifecycle. We further identify pivotal
challenges and future research trajectories such as robotic automation,
self-improvement, and ethical governance. Overall, this survey provides a
conceptual architecture and strategic foresight to navigate and shape the
future of AI-driven scientific discovery, fostering both rapid innovation
and responsible advancement.


Date:                   Tuesday, 14 October 2025

Time:                   4:00pm - 6:00pm

Venue:                  Room 5501
                        Lifts 25/26

Committee Members:      Dr. Yangqiu Song (Supervisor)
                        Dr .Junxian He (Chairperson)
                        Dr. May Fung