From Conceptualization to Metaphysical Reasoning: Frameworks and Benchmarks Towards Generalizable Reasoning

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "From Conceptualization to Metaphysical Reasoning: Frameworks and
Benchmarks Towards Generalizable Reasoning"

By

Mr. Weiqi WANG


Abstract:

Large language models (LLMs) show strong reasoning across many tasks, yet
their reliability can vary when assumptions change, inputs shift distribution,
or familiar knowledge must be recomposed in novel ways. This thesis argues
that a key organizing principle for improving such generalization is
conceptualization: the ability to abstract concrete events and entities into
reusable concepts, and to instantiate those concepts in new situations. We
develop a unified paradigm in which conceptualization structures how
commonsense knowledge is represented, acquired, scaled, modified, and
evaluated beyond surface competence.

We first systematize concept-centric methods for LLMs and formalize a
conceptualization–instantiation cycle over commonsense knowledge bases
(CSKBs) as a lens spanning generation, question answering, and knowledge
manipulation. Building on this lens, we introduce approaches that construct
and exploit concept-structured event and entity knowledge to improve
generative commonsense inference and zero-shot commonsense question
answering, showing that concept-level structure can strengthen reasoning
without relying solely on model scale. To address limited CSKB coverage, we
propose a scalable distillation framework that extracts large volumes of
concept-structured knowledge from strong LLMs and uses critic-style
filtering to retain plausible, useful knowledge, expanding coverage while
preserving quality.

Beyond acquisition, we study controlled knowledge modification via a
concept-level editing framework that couples automated plausibility
verification with concept-aware rewriting, improving both factuality and
downstream utility. Finally, we introduce metaphysical reasoning as a
concept-driven stress test: reasoning about improbable or counterfactual
changes to conceptualized events. We provide a benchmark that decomposes
this challenge into discriminating event, inference, and transition validity
under controlled distribution shifts, revealing persistent gaps between
apparent competence and deeper conceptual understanding.

Together, these contributions advance frameworks, resources, and evaluations
that push LLMs toward more robust, generalizable reasoning grounded in
concept-level structure.


Date:                   Thursday, 26 March 2026

Time:                   9:00am - 11:00am

Venue:                  Room 2132C
                        Lift 22

Chairman:

Committee Members:      Dr. Yangqiu SONG (Supervisor)
                        Dr. Long CHEN
                        Prof. Raymond WONG
                        Dr. Lixue Sherry CHENG (CHEM)
                        Prof. Wenjie LI (PolyU)