Author

Yongxin Wang

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#artificial intelligence Preprint Oct 2026

Trial by Trace: Benchmarking AI Agent Harnesses for Reliability and Failure Attribution

LLM agents are increasingly used to solve real-world problems, and their effectiveness depends on both the underlying models and the harnesses that support their operation. However, most existing benchmarks focus on model capabilities and overall agent performance, with limited attention to the evaluation of agent harn...

Zhi-Yong Cao, Heng-Jie Zhu, Yong-Xin Wang et al. · 2 citations

Conference Open access 2026

AutoSUIT Bench - Automated Security UnIt Test Benchmark for LLM Coding

Upon benchmarking against LLMs, it is found that functionality pass rate is consistently higher than vulnerability pass rate for all programming languages, highlighting the necessity of vulnerable code benchmarks with larger CWE coverage.

Samuel Osebe, Fan Yang, Junyi Li et al. · Annual Meeting of the Associ... · 0 citations