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Si-Min Chen

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#artificial intelligence Preprint Oct 2026

TestJack: Should you trust the results in coding benchmarks? Agentic Coding Benchmarks Auditing via Evaluator Evolution

Large language model (LLM) agents are rapidly reshaping software engineering, accompanied by an explosion of new code benchmarks. Yet nearly all existing benchmarks still rely on the same decades-old criterion: a solution is correct if it passes a fixed set of unit tests. Such tests are often insufficient: they check o...

Shuang-Jie Yao, Hao Wang, Koushik Sen et al. · 0 citations
Jul 2026

When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs

It is found that defenses rarely improve downstream capability, but instead vary in how they trade safety gains against usability and efficiency as well as performance impact, over-refusal on benign inputs, and inference cost.

Tong Zhang, Zexin Li, Si-Min Chen et al. · 0 citations

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