From the 2 of 16 linked papers with an AI index.
16 papers
Asymmetric Capacity Allocation in Self-Refinement Pipelines
Zhuoyi Yang, Ian G. Harris, Salar Hashemitaheri +7
Self-refinement, typically structured as generation, critique, and revision, is a widely adopted paradigm for improving LLM generation and serves as a core mechanism in many LLM ag…
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
PolyQ is a co-designed compiler and quantization framework that assigns per‑channel bit‑widths to LLM activations on CPUs, enabling fine‑grained fractional‑bit precision while keep…
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…