5 papers
The Mask Is Not the Model: Auditing Prefix Invariance in Attention, State-Space, and Hybrid Sequence Models
Taebong Kim, Youngsik Hong, Minsik Kim +3
Hybrid sequence models must satisfy prefix invariance: representations at position t must not depend on future inputs, yet this is rarely verified. We formalize prefix invariance a…
A Numerical Realization of Suzuki's Weil-Quadratic-Form Operator: The Archimedean Spectral Law, its Universality, and an Operator Form of Weil's Positivity Criterion
Taebong Kim, Youngsik Hong, Minsik Kim +3
This paper presents the first numerical realization of Suzuki's Weil-Quadratic-Form operator, a candidate for the Hilbert--Pólya program linking spectral positivity to the Riemann…
Quantum Cryptanalysis on IBM Quantum Hardware: Extending Even--Mansour Period Recovery from to
Taebong Kim, Youngsik Hong, Minsik Kim +4
We report genuine-un-compiled, textbook-faithful-quantum cryptanalysis of symmetric-cipher structures executed on real IBM quantum hardware (ibm\_kingston, Heron generation). Using…
Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning
Taebong Kim, Youngsik Hong, Minsik Kim +4
We present Darwin Family, a framework for training-free evolutionary merging of large language models via gradient-free weight-space recombination. We ask whether frontier-level re…
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning
Jaewon Jang, Bonjun Choi
Federated learning ensures the privacy of clients by conducting distributed training on individual client devices and sharing only the model weights with a central server. However,…