Grounding Judgement in Structure and Evidence: From Structured Generation to Agentic Reasoning

PhD Thesis Proposal Defence


Title: "Grounding Judgement in Structure and Evidence: From Structured 
Generation to Agentic Reasoning"

by

Mr. Zheye DENG


Abstract:

Large language models (LLMs) can produce fluent outputs that appear 
trustworthy, but fluency cannot establish whether a structured artifact 
respects its source or an agent's decision follows from its interaction 
history. The failures take different forms. A plausible graph relation may 
conflict with its surrounding graph, a well-formed table may omit source 
information, and a successful decision may hide an unsupported sequence of 
tool calls. This dissertation develops methods for judging such outputs and 
actions through task structure and evidence that can be checked.

One line of work focuses on structured artifacts, covering the verification of 
graph relations, the generation of tables from long documents, and the 
evaluation of multiple visual formats. In commonsense knowledge graphs, 
relation quality is assessed through edge semantics, mined logical rules, and 
local neighborhoods, while an intermediate tuple representation for 
long-document summarization exposes event selection, deduplication, and 
aggregation before the final table is produced. Evaluation extends to tables, 
graphs, and charts through separate criteria for faithfulness and coherence, 
both validated against human ratings.

The second line studies agentic reasoning, where evidence accumulates through 
interaction. A financial decision policy is trained with market outcomes and 
process checks on its information requests and tool calls. A competitive 
programming verifier returns a verdict and diagnosis before constructing an 
executable counterexample for a suspected fault. If the counterexample is 
legal and breaks the candidate under execution, it supplies concrete evidence 
of the program failure. Across both lines, task-specific evidence anchors each 
judgment and allows it to be checked independently of the model's own 
explanation.


Date:                   Wednesday, 16 September 2026

Time:                   9:00am - 11:00am

Venue:                  Room 3494
                        Lift 25/26

Committee Members:      Dr. Yangqiu Song (Supervisor)
                        Prof. Raymond Wong (Chairperson)
                        Dr. Chaojian Li