AgentBoard: An Analytical Evaluation Board for Multi-Turn LLM Agents
This forum post indexes the arXiv paper AgentBoard: An Analytical Evaluation Board of Multi-Turn LLM Agents (arXiv:2401.13178), authored by Chang Ma, Junlei Zhang, Zhihao Zhu, Cheng Yang, Yujiu Yang, Yaohui Jin and colleagues (9 authors in total). It is relevant to the evaluation of LLM agents, agentic search, and search/recommendation systems.
Key points
- Problem addressed: Most existing LLM agent benchmarks report only final success rates. This hides *where* in a multi-turn interaction an agent fails. AgentBoard introduces a process-oriented, analytical evaluation methodology for multi-turn agents.
- Progress Rate metric: The paper's core contribution is a fine-grained progress metric that measures the fraction of subgoals an agent completes at each turn, enabling turn-by-turn comparison of agent trajectories rather than binary end-state scoring.
- Coverage of diverse agent tasks: The benchmark spans multiple interaction categories, including embodied environments, web/GUI interaction, tool use, math reasoning, and game environments, evaluated under a unified protocol and interface.
- Open evaluation board: AgentBoard ships as an open-source evaluation platform with uniform agent interfaces, turn-level logging, and visualization, designed for reproducible, extensible agent research.
- Findings: Evaluations of leading open-source and proprietary LLMs show that model strength on chat benchmarks does not uniformly translate into multi-turn agent competence; process-level analysis exposes failure patterns that end metrics conceal.
- Quantitative results (leaderboard scores per model and per task) should be verified against the original PDF tables; this page is based on the paper's public metadata and abstract.
- For follow-up reading, compare with related evaluation work such as RAG evaluation surveys and automated RAG evaluation frameworks (e.g., ARES).
- Paper: AgentBoard: An Analytical Evaluation Board of Multi-Turn LLM Agents — https://arxiv.org/abs/2401.13178
- Related entries: Evaluation of Retrieval-Augmented Generation: A Survey (arXiv:2405.07437); ARES: An Automated Evaluation Framework for RAG (arXiv:2311.09476); Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks (arXiv:2505.24876)
Relevance to search and recommendation research
From the perspective of agentic search and RAG evaluation, AgentBoard represents the shift from static, single-shot metrics (e.g., nDCG, MRR) toward process-level evaluation of systems that retrieve, plan, and call tools iteratively. Its turn-level scoring philosophy parallels trends in evaluating multi-hop retrieval chains, citation accuracy, and task success rates in conversational search.