pcbGPT is a grounded system that automatically generates editable KiCad PCB schematics from natural language requirements, achieving high accuracy on complex embedded design tasks.

The paper proposes a visual program synthesis framework using a VLM to generate accurate training data for semiconductor inspection, mitigating the sim-to-real gap by applying input binarization to st…

The paper introduces ProofLoop, a novel ReAct agent that uses a solver-in-the-loop approach to automatically generate and formally verify SystemVerilog Assertions (SVA) from natural language specifica…

This review analyzes the dual impact of integrating Large Language Models (LLMs) into hardware design, detailing both their transformative potential in EDA and the critical security vulnerabilities th…

This paper systematizes two decades of hardware reverse engineering research by analyzing 187 publications, identifying key technical methods and recommending improvements for reproducibility, standar…

The paper introduces MUSE, a comprehensive benchmark that evaluates Text-to-CAD generation by assessing complex assemblies based on functionality, manufacturability, and assemblability, moving beyond…

The paper proposes projectional decoding, a novel framework that integrates a partial graph model alongside text generation to ensure the semantic validity of LLM-generated software artifacts.

StepPRM-RTL is a novel framework that enhances LLM-based RTL code generation for digital hardware designs.

HighTide is an evolving, AI-assisted, open-source benchmark suite for VLSI design, providing a comprehensive and scalable platform for hardware development.

The paper introduces CASS-RTL, a novel, model-agnostic framework that enhances the functional correctness of Large Language Models (LLMs) generating Register-Transfer Level (RTL) code by leveraging th…

The paper introduces 3DCodeBench, a systematic benchmark and platform for evaluating Vision-Language Model (VLM) agents' ability to generate procedural 3D models from text and images using code.

Assertain is an automated framework that uses large language models and design analysis to generate high-quality, executable security assertions for hardware designs, significantly outperforming state…

The paper introduces RadioMaster, a novel multi-agent system that successfully translates high-level user intents into physically viable, real-world radio signals, significantly outperforming existing…

The paper introduces DOMINO, a novel inductive framework that synthesizes domain-specific data for LLMs using only reference examples, significantly improving performance on challenging, implicitly de…

This paper introduces an agentic LLM-driven framework that automates the generation of functionally correct and security-relevant hardware netlist obfuscation for protecting intellectual property.

PhyDrawGen is a neuro-symbolic pipeline that generates physically accurate diagrams from natural language by explicitly enforcing physical laws and geometric constraints, significantly outperforming c…

This paper details the design and evolution of a Hardware Reverse Engineering (HRE) course, providing key lessons for educators teaching rapidly changing technical domains.

The paper analyzes LLM vulnerability detection using mechanistic interpretability, finding that models primarily rely on safety detectors rather than direct vulnerability signature recognition.

The paper introduces MAVEN, a lightweight symbolic reasoning scaffold that significantly improves the generalization and end-to-end success rate of large language models in complex, multi-step tool-ca…

The paper introduces CodeGolf Bench, a novel multi-language benchmark using code golf to measure LLMs' ability to generate highly concise and efficient code, showing that reasoning models significantl…