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Researcher profile

Gyubin Lee

Gyubin Lee contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceComputer Visioncond-mat.str-elcond-mat.supr-coneess.ASMachine LearningMultimediaSound

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Published work

3 published item(s)

preprint2026arXiv

CounterFlow: A Two-Phase Inference-Time Sampling for Counterfactual Video Foley Generation

We investigate Counterfactual Video Foley Generation, which aims to adopt a sound-source identity that contradicts the visual evidence while remaining temporally synchronized to a silent video. Existing Video&Text-to-Audio (VT2A) models struggle with this, often remaining anchored to the visually implied sound source when video and text contents disagree. We present ConterFlow, an inference-time dual-phase sampling scheme for pretrained flow-matching VT2A models. Phase 1 builds a video-derived temporal structure while suppressing the visually implied source; Phase 2 drops video conditioning to focus entirely on shaping audio timbre toward the target prompt. ConterFlow substantially improves counterfactual Video Foley generation compared to naive negative prompting and state-of-the-art baselines. To evaluate replacement quality, we propose a metric leveraging a text-audio co-embedding space to measure both target-prompt evidence and residual visually implied source leakage. Video demonstrations and code are available at https://gyubin-lee.github.io/counterflow-demo/

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preprint2026arXiv

Learning to Theorize the World from Observation

What does it mean to understand the world? Contemporary world models often operationalize understanding as accurate future prediction in latent or observation space. Developmental cognitive science, however, suggests a different view: human understanding emerges through the construction of internal theories of how the world works, even before mature language is acquired. Inspired by this theory-building view of cognition, we introduce Learning-to-Theorize, a learning paradigm for inferring explicit explanatory theories of the world from raw, non-textual observations. We instantiate this paradigm with the Neural Theorizer (NEO), a probabilistic neural model that induces latent programs as a learned Language of Thought and executes them through a shared transition model. In NEO, a theory is represented as an executable, compositional program whose learned primitives can be systematically recombined to explain novel phenomena. Experiments show that this formulation enables explanation-driven generalization, allowing observations to be understood in terms of the programs that generate them.

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preprint2020arXiv

A weak topological insulator state in quasi-one-dimensional superconductor TaSe$_3$

A well-established way to find novel Majorana particles in a solid-state system is to have superconductivity arising from the topological electronic structure. To this end, the heterostructure systems that consist of normal superconductor and topological material have been actively explored in the past decade. However, a search for the single material system that simultaneously exhibits intrinsic superconductivity and topological phase has been largely limited, although such a system is far more favorable especially for the quantum device applications. Here, we report the electronic structure study of a quasi-one-dimensional (q1D) superconductor TaSe$_3$. Our results of angle-resolved photoemission spectroscopy (ARPES) and first-principles calculation clearly show that TaSe$_3$ is a topological superconductor. The characteristic bulk inversion gap, in-gap state and its shape of non-Dirac dispersion concurrently point to the topologically nontrivial nature of this material. The further investigations of the Z$_2$ indices and the topologically distinctive surface band crossings disclose that it belongs to the weak topological insulator (WTI) class. Hereby, TaSe$_3$ becomes the first verified example of an intrinsic 1D topological superconductor. It hopefully provides a promising platform for future applications utilizing Majorana bound states localized at the end of 1D intrinsic topological superconductors.

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