Author

Minghao Shao

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Preprint Aug 2026

FinHardBench: Can LLMs Generate Latency-Aware Hardware for Financial Computing?

Can large language models generate not just correct, but fast hardware? This paper investigates the question in financial FPGA design, where 5-10 nanoseconds of latency determines competitive advantage and designs iterate continuously as protocols, strategies, and regulations evolve. FinHardBench, a benchmark of 33 fin...

Weimin Fu, He-Jia Zhang, Ming-Hao Shao et al. · 0 citations

Preprint Aug 2026

How Do LLM Agents Actually Get the Flag? Trace-Level Provenance for Agentic Offensive Security Evaluation

CTF-ABACUS is introduced, a trace-based agent auditing framework that reconstructs each run as an evidence-grounded solve profile that provides a basis for designing benchmarks that better isolate the offensive capabilities of autonomous language-model agents.

Kimberly Milner, Ming-Hao Shao, Nanda Rani et al. · 1 citation

Open access 2026

From Trace to Line: An Empirical Study of What Drives LLM-Based OSS Vulnerability Localization

This paper introduces T2L (Trace-to-Line), a reproducible research framework that narrows repository-scale code into candidate vulnerable lines through AST-based chunking, structured diagnostic information collection, and evidence-guided refinement that improves trace-to-line localization.

Hao-Ran Xi, Ming-Hao Shao, Brendan Dolan-Gavitt et al. · IEEE Access · 0 citations