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cs.AI2026
SEA-Eval: A Benchmark for Evaluating Self-Evolving Agents Beyond Episodic Assessment
Sihang Jiang, Lipeng Ma, Zhonghua Hong +9
Current LLM-based agents demonstrate strong performance in episodic task execution but remain constrained by static toolsets and episodic amnesia, failing to accumulate experience…
cs.AI2026
RubricEval: A Rubric-Level Meta-Evaluation Benchmark for LLM Judges in Instruction Following
Tianjun Pan, Xuan Lin, Wenyan Yang +7
Rubric-based evaluation has become a prevailing paradigm for evaluating instruction following in large language models (LLMs). Despite its widespread use, the reliability of these…