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arXivlens breakdown of this paper 👉 https://arxivlens.com/PaperView/Details/agent-as-a-judge-6663-bf4ecd9d

Executive Summary
Detailed Breakdown
Practical Applications

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Papers

arxiv:2601.05111

Agent-as-a-Judge

Published on Jan 8

· Submitted by

taesiri on Jan 9

ModalityDance

Upvote

Authors:

Runyang You ,

Hongru Cai ,

Abstract

Large language models face limitations in evaluating complex, multi-step tasks, prompting the development of agent-based evaluation systems that utilize planning, tool-augmented verification, and multi-agent collaboration for more robust assessments.

AI-generated summary

LLM-as-a-Judge has revolutionized AI evaluation by leveraging large language models for scalable assessments. However, as evaluands become increasingly complex, specialized, and multi-step, the reliability of LLM-as-a-Judge has become constrained by inherent biases, shallow single-pass reasoning, and the inability to verify assessments against real-world observations. This has catalyzed the transition to Agent-as-a-Judge, where agentic judges employ planning, tool-augmented verification, multi-agent collaboration, and persistent memory to enable more robust, verifiable, and nuanced evaluations. Despite the rapid proliferation of agentic evaluation systems, the field lacks a unified framework to navigate this shifting landscape. To bridge this gap, we present the first comprehensive survey tracing this evolution. Specifically, we identify key dimensions that characterize this paradigm shift and establish a developmental taxonomy. We organize core methodologies and survey applications across general and professional domains. Furthermore, we analyze frontier challenges and identify promising research directions, ultimately providing a clear roadmap for the next generation of agentic evaluation.