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cs.SE2026
VerifierBench: Benchmarking the Generation of Executable Verifiers for LLM Agents
Jianing Yin, Ziyi Chen, Hao Wang +4
Agents need reliable ways to validate their outputs, both when completing tasks and when filtering synthetic training data. We study verifier generation: from the instruction and e…
cs.SE2026
TestJack: Should you trust the results in coding benchmarks? Agentic Coding Benchmarks Auditing via Evaluator Evolution
Shuangjie Yao, Hao Wang, Koushik Sen +3
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 t…
cs.SE2026
StepCodeReasoner: Aligning Code Reasoning with Stepwise Execution Traces via Reinforcement Learning
Hao Wang, Rui Li, Lei Sha +1
Existing code reasoning methods primarily supervise final code outputs, ignoring intermediate states, often leading to reward hacking where correct answers are obtained through inc…