AgentAtlas: Beyond Outcome Leaderboards for LLM Agents
The paper titled 'AgentAtlas: Beyond Outcome Leaderboards for LLM Agents' addresses the limitations of current benchmarks for evaluating large language model agents. It proposes a new framework that includes various taxonomies and methodologies to better assess agent performance. The authors demonstrate their approach through a series of experiments, highlighting the need for more comprehensive evaluation metrics.
- ▪Current benchmarks for large language model agents are fragmented and focus on different metrics.
- ▪AgentAtlas introduces a six-state control-decision taxonomy and a nine-category trajectory-failure taxonomy.
- ▪The study shows that removing explicit labels significantly reduces trajectory accuracy across models.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.20530 (cs) [Submitted on 19 May 2026] Title:AgentAtlas: Beyond Outcome Leaderboards for LLM Agents Authors:Parsa Mazaheri, Kasra Mazaheri View a PDF of the paper titled AgentAtlas: Beyond Outcome Leaderboards for LLM Agents, by Parsa Mazaheri and 1 other authors View PDF HTML (experimental) Abstract:Large language model agents now act on codebases, browsers, operating systems, calendars, files, and tool ecosystems, but the benchmarks used to evaluate them are fragmented: each emphasizes a different unit of measurement (final task success, tool-call validity, repeated-pass consistency, trajectory safety, or attack robustness).
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
