5 papers
Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning
Muhammad Usama, Dong Eui Chang
Large language models trained under diverse objectives and architectures have been shown to develop increasingly similar internal representations, an observation formalized as the…
Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Design
Elias Berger, Muhammad Usama, Jan Mehlstäubl +2
Large Language Models (LLMs) can generate Computer-Aided Design (CAD), yet lack physical comprehension required for reliable engineering design. Instead of attempting to implicitly…
Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization
Muhammad Usama, Dong Eui Chang
Equalizer parameter optimization is critical for signal integrity in high-speed memory systems operating at multi-gigabit data rates. However, existing methods suffer from computat…
Deep Reinforcement Learning-Based DRAM Equalizer Parameter Optimization Using Latent Representations
Muhammad Usama, Dong Eui Chang
Equalizer parameter optimization for signal integrity in high-speed Dynamic Random Access Memory systems is crucial but often computationally demanding or model-reliant. This paper…
Learning High-Quality Latent Representations for Anomaly Detection and Signal Integrity Enhancement in High-Speed Signals
Muhammad Usama, Hee-Deok Jang, Soham Shanbhag +3
This paper addresses the dual challenge of improving anomaly detection and signal integrity in high-speed dynamic random access memory signals. To achieve this, we propose a joint…