Author

Shuang-Jie Yao

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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

#artificial intelligence Preprint Oct 2026

Cost-Efficient Theorem Proving via Agent Orchestration in Program Verification

Program verification establishes software correctness through machine-checkable proofs constructed in theorem provers. It's a guarantee especially valuable for code generated by large language models (LLMs), which is fluent but carries no assurance of correctness. Almost all existing provers, however, pursue pass rates...

Shuang-Jie Yao, Nikolaus Holzer, Mark Santolucito et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Similarity Is Not Validity: Defending LLM Semantic Caches Against Poisoning

This work proposes a novel defense that recovers necessary information needed to distinguish valid from invalid cache hits from the raw text of the cache key, and proves that Deletion Gain stays positive when a deletion leaves text close enough to the rewrite, and searches for such deletions with a sliding window.

Zi-Han Zhang, Shuang-Jie Yao, Ze-Sen Liu et al. · 0 citations