From Self-Aware to Human-Aware: A Survey on Trustworthy LLM Agents

PhD Qualifying Examination


Title: "From Self-Aware to Human-Aware: A Survey on Trustworthy LLM 
Agents"

by

Mr. Zhitao HE


Abstract:

Large language model (LLM) agents are increasingly deployed as interactive 
systems that reason, use tools, communicate with users, and act in 
consequential environments. This shift raises a central question for 
trustworthy AI: what must an agent be aware of before it can act reliably on 
behalf of humans? This survey organizes recent work around an 
awareness-centered taxonomy of trustworthy LLM agents. We review three 
complementary forms of awareness: self-awareness, where agents recognize their 
own knowledge boundaries, uncertainty, capability limits, and 
self-verification mechanisms; environment-awareness, where agents ground 
decisions in external constraints, tools, domains, and verifiable 
consequences; and human-awareness, where agents model mental states, user 
intent, preferences, collaboration, and group dynamics. Rather than treating 
trustworthiness as a flat collection of safety, robustness, and alignment 
techniques, we argue that these dimensions can be understood as failures or 
successes of agent awareness. For each form of awareness, we organize the 
relevant methods, benchmarks, and training approaches within the corresponding 
section. We conclude with open challenges in distinguishing genuine awareness 
from performative claims, scaling evaluation to realistic environments, and 
balancing helpful autonomy with safety-critical boundaries.


Date:                   Monday, 15 June 2026

Time:                   5:00pm - 6:00pm

Venue:                  Room 3494
                        Lift 25/26

Committee Members:      Prof. Siu-Wing Cheng (Supervisor)
                        Dr. May Fung (Co-Supervisor)
                        Prof. James Kwok (Chairperson)
                        Dr. Sirui Han (EMIA)