Agent-as-a-Judge
Agent-as-a-Judge: the next step in trustworthy AI evaluation
As AI tasks get more complex, one-shot model graders fall short—biased, shallow, and unable to check the real world. This new survey traces the shift from LLM-as-a-Judge to agentic judges that plan, use tools, collaborate, and remember—making evaluations more robust and verifiable.
- A unified framework and developmental taxonomy
- Core methods: planning, tool-augmented verification, multi-agent debate, persistent memory
- Applications across general and professional domains
- Frontier challenges and a research roadmap
The goal: evaluations you can trust, not just outputs you can read.
Paper by Runyang You, Hongru Cai, Caiqi Zhang, Qiancheng Xu, Meng Liu, Tiezheng Yu, Yongqi Li, Wenjie Li (cs.CL, cs.AI). Read: https://arxiv.org/abs/2601.05111v1
Paper: https://arxiv.org/abs/2601.05111v1
Register: https://www.AiFeta.com
#AI #LLM #Agents #AgenticAI #Evaluation #NLP #MachineLearning #AIEthics #Benchmarks #Research
