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Hyunwoo Oh

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Book Open access Aug 2026

Vector-Space Projection and Unified Complex-Valued Acceleration for Scaling Matrix-Based Brain-Inspired Representations

This work proposes a flattening methodology that preserves GHRR's matrix-based encoding while executing training and inference directly in vector space, functionally equivalent to FHRR inference yet free of permutation logic.

William Youngwoo Chung, Hyunwoo Oh, Calvin Yeung et al. · 0 citations
#artificial intelligence Preprint Sep 2026

CyberWorld: World Models for Sample-Efficient Autonomous Cyber Defense

This work introduces CyberWorld, a Dreamer-style world modeling framework that learns latent cyber dynamics from vector, graph, textual, and multimodal representations of the defended network, and identifies world representation as a central design axis for robustness and scalability.

Ryozo Masukawa, Sanggeon Yun, Raheeb Hassan et al. · 0 citations
Jul 2026

ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM

ExaGEMM, a workload-aware codesign and exploration framework for CPU-native low-bit GEMM via register-resident LUT execution, improves latency by 13.29x over software-only baselines, while showing that workload-aware frontier selection is especially important for mixed-precision LLM workloads.

Hyunwoo Oh, Suyeon Jang, Hanning Chen et al. · 0 citations
#machine learning Preprint Aug 2026

Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)

Empirical evaluations reveal a fundamental brittleness in existing defenses: with a single trainable 7B planner, Trident reduces blue agent defensive performance by an average of 522% compared to static red agent baselines while autonomously discovering emergent behaviors such as decoy avoidance and adaptive state prio...

Ryozo Masukawa, Ian Bryant, Armita Kazeminajafabadi et al. · 1 citation
#small language model Preprint Aug 2026

Asymmetric Capacity Allocation in Self-Refinement Pipelines

It is concluded that larger generators and refiners generally improve the pipeline, whereas an undersized refiner can even harm performance, and that model capacity should not be allocated uniformly across self-refinement pipelines.

Zhuoyi Yang, Ian G. Harris, Salar Hashemitaheri et al. · 0 citations
Jul 2026

PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference

PolyQ, a CPU-oriented compiler/quantization co-design for activation-aware channel-wise bit allocation under a user-specified average-bit budget, shows that fractional-bit CPU deployment is practical, predictable, and energy-efficient across diverse edge targets.

Hyunwoo Oh, Suyeon Jang, Hanning Chen et al. · 0 citations
#artificial intelligence Preprint Aug 2026

Vector Symbolic Policy Gradient

Vector-Symbolic Policy Gradient (VSPG), a discrete-action actor that represents each action by a unit-norm hypervector and scores it by similarity to the encoded state, connects VSA action memories, log-linear policy gradients, and kernel policy search while providing a quantitative robustness guarantee.

Ryozo Masukawa, Sanggeon Yun, Sungheon Jeong et al. · 0 citations

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