Ramesh Karri

Vineet Bhat, Shiqing Wei, Ali Umut Kaypak et al.

Reconstructing numerical simulations from control systems research papers is often hindered by underspecified parameters and ambiguous implementation details. We define the task of Paper to Simulation Recoverability, the ability of an automated system to generate executable code that faithfully reproduces a paper's results. We curate a benchmark of 500 papers from the IEEE Conference on Decision and Control (CDC) and propose RESCORE, a three component LLM agentic framework, Analyzer, Coder, and Verifier. RESCORE uses iterative execution feedback and visual comparison to improve reconstruction fidelity. Our method successfully recovers task coherent simulations for 40.7% of benchmark instances, outperforming single pass generation. Notably, the RESCORE automated pipeline achieves an estimated 10X speedup over manual human replication, drastically cutting the time and effort required to verify published control methodologies. We will release our benchmark and agents to foster community progress in automated research replication.

Weihua Xiao, Derek Ekberg, Siddharth Garg et al.

SystemVerilog Assertions (SVAs) are critical for verifying the correctness of hardware designs, but manually writing them from natural language property descriptions, i.e., NL2SVA, remains a labor-intensive and error-prone task. Recent advances in large language models (LLMs) offer opportunities to automate this translation. However, existing models still struggle with understanding domain-specific syntax and semantics. To enhance LLM performance in NL2SVA, we propose a customized retrieval-augmented generation (RAG) framework and a synthetic fine-tuning dataset that together improve LLM's performance. To further improve lightweight models over NL2SVA, our fine-tuning dataset provides prompt-guided explanations that teach LLMs the layer-by-layer construction process of concurrent SVAs, enabling supervised fine-tuning that greatly improves syntax and functionality accuracy. To evaluate the performance of LLMs over NL2SVA, we construct the largest evaluation dataset for NL2SVA, comprising 40 Verilog designs and 229 formally verified SVAs with detailed annotations. Experimental results show that our customized RAG framework increases the number of functionality matched SVAs by 58.42% over GPT-4o-mini, while Qwen2.5-Coder-7B-Instruct fine-tuned on our fine-tuning dataset and integrated with HybridRetrieval achieves a 59.05% over the base Qwen model.