5 citations · 20 across the 34 of their papers we have counts for
40 papers
CyberWorld: World Models for Sample-Efficient Autonomous Cyber Defense
Ryozo Masukawa, Sanggeon Yun, Raheeb Hassan +3
Deep reinforcement learning has become a prominent approach to autonomous cyber defense. Existing methods are predominantly model-free and consequently require extensive environmen…
World State Generator
Sungheon Jeong, Sanggeon Yun, Ryozo Masukawa +3
Language agents solve complex tasks through plans and actions. A single step the world refuses puts the goal out of reach, and what the agent does next decides the task. Prompted p…
AudioLens: Multi-Perspective Speech Clustering with Reasoning Audio-Language Models
Wenjun Huang, Qiaosong Chu, Tiger Shao +11
Audio clustering is a fundamental task for organizing rapidly growing speech collections, supporting applications such as conversational analysis and speech-driven discovery. Howev…
Vector Symbolic Policy Gradient
Ryozo Masukawa, Sanggeon Yun, SungHeon Jeong +6
We answer this question with Vector-Symbolic Policy Gradient (VSPG), a discrete-action actor that represents each action by a unit-norm hypervector and scores it by similarity to t…
Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)
Ryozo Masukawa, Ian Bryant, Armita Kazeminajafabadi +6
Autonomous cyber defense systems based on Deep Reinforcement Learning (DRL) have attracted significant research attention, yet remain evaluated almost exclusively against static, h…
ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM
Hyunwoo Oh, Suyeon Jang, Hanning Chen +3
Low-bit GEMM is increasingly central to efficient ML inference, yet very-low-bit execution remains a poor fit for conventional CPUs. Practical deployment spans fragmented regimes-f…
PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference
Hyunwoo Oh, Suyeon Jang, Hanning Chen +4
CPUs are the most universal target for on-device LLM inference, but existing low-bit quantization methods offer either coarse operating points or fine-grained mixed precision that…
Qubit-Efficient Quantum Search for Hyperdimensional Decomposition via Logarithmic Encoding
Sanggeon Yun, Hyunwoo Oh, Ryozo Masukawa +2
Hyperdimensional Computing (HDC) represents symbols using high-dimensional hypervectors of dimension . In hypervector decomposition, the objective is to recover constituent…
FusionSense: Tri-Stage Near-Sensor Learning for Runtime-Adaptive Multimodal Edge Intelligence
Sanggeon Yun, Ryozo Masukawa, Minhyoung Na +5
Autonomous systems and smart-industry deployments increasingly split computation across near-sensor, edge, and cloud resources, where tight energy, latency, and reliability budgets…
State-Centric Decision Process
Sungheon Jeong, Ryozo Masukawa, Sanggeon Yun +2
Language environments such as web browsers, code terminals, and interactive simulations emit raw text rather than states, and provide none of the runtime structure that MDP analysi…
TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI
Hyunwoo Oh, Hanning Chen, Sanggeon Yun +5
Multimodal stacks that mix ViTs, CNNs, GNNs, and transformer NLP strain embedded platforms because their compute/memory patterns diverge and hard real-time targets leave little sla…
TorR: Towards Brain-Inspired Task-Oriented Reasoning via Cache-Oriented Algorithm-Architecture Co-design
Hyunwoo Oh, SungHeon Jeong, Suyeon Jang +4
Task-oriented object detection (TOOD) atop CLIP offers open-vocabulary, prompt-driven semantics, yet dense per-window computation and heavy memory traffic hinder real-time, power-l…
MERIT: Multi-domain Efficient RAW Image Translation
Wenjun Huang, Shenghao Fu, Yian Jin +10
RAW images captured by different camera sensors exhibit substantial domain shifts due to varying spectral responses, noise characteristics, and tone behaviors, complicating their d…
-Musketeers: Reinforcement Learning Shapes Collaboration Among Language Models
Ryozo Masukawa, Sanggeon Yun, Hyunwoo Oh +8
Recent progress in reinforcement learning with verifiable rewards (RLVR) shows that small, specialized language models (SLMs) can exhibit structured reasoning without relying on la…
Fair Context Learning for Evidence-Balanced Test-Time Adaptation in Vision-Language Models
Sanggeon Yun, Ryozo Masukawa, SungHeon Jeong +3
Vision-Language Models (VLMs) such as CLIP enable strong zero-shot recognition but suffer substantial degradation under distribution shifts. Test-Time Adaptation (TTA) aims to impr…
HopFormer: Sparse Graph Transformers with Explicit Receptive Field Control
Sanggeon Yun, Raheeb Hassan, Ryozo Masukawa +2
Graph Transformers typically rely on explicit positional or structural encodings and dense global attention to incorporate graph topology. In this work, we show that neither is ess…
Internal Flow Signatures for Self-Checking and Refinement in LLMs
Sungheon Jeong, Sanggeon Yun, Ryozo Masukawa +3
Large language models can generate fluent answers that are unfaithful to the provided context, while many safeguards rely on external verification or a separate judge after generat…
QUILL: An Algorithm-Architecture Co-Design for Cache-Local Deformable Attention
Hyunwoo Oh, Hanning Chen, Sanggeon Yun +5
Deformable transformers deliver state-of-the-art detection but map poorly to hardware due to irregular memory access and low arithmetic intensity. We introduce QUILL, a schedule-aw…
T-SAR: A Full-Stack Co-design for CPU-Only Ternary LLM Inference via In-Place SIMD ALU Reorganization
Hyunwoo Oh, KyungIn Nam, Rajat Bhattacharjya +7
Recent advances in LLMs have outpaced the computational and memory capacities of edge platforms that primarily employ CPUs, thereby challenging efficient and scalable deployment. W…
Draft and Refine with Visual Experts
Sungheon Jeong, Ryozo Masukawa, Jihong Park +5
While recent Large Vision-Language Models (LVLMs) exhibit strong multimodal reasoning abilities, they often produce ungrounded or hallucinated responses because they rely too heavi…
LogHD: Robust Compression of Hyperdimensional Classifiers via Logarithmic Class-Axis Reduction
Sanggeon Yun, Hyunwoo Oh, Ryozo Masukawa +3
Hyperdimensional computing (HDC) suits memory, energy, and reliability-constrained systems, yet the standard "one prototype per class" design requires memory (with clas…
DecoHD: Decomposed Hyperdimensional Classification under Extreme Memory Budgets
Sanggeon Yun, Hyunwoo Oh, Ryozo Masukawa +1
Decomposition is a proven way to shrink deep networks without changing input-output dimensionality or interface semantics. We bring this idea to hyperdimensional computing (HDC), w…
MissionHD: Hyperdimensional Refinement of Distribution-Deficient Reasoning Graphs for Video Anomaly Detection
Sanggeon Yun, Raheeb Hassan, Ryozo Masukawa +2
LLM-generated reasoning graphs, referred to as mission-specific graphs (MSGs), are increasingly used for video anomaly detection (VAD) and recognition (VAR). However, they are typi…
A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection
Sanggeon Yun, Ryozo Masukawa, Hyunwoo Oh +2
Deep neural networks (DNNs) are highly susceptible to adversarial examples---small, malicious perturbations that can cause incorrect predictions. We introduce a lightweight, plug-i…