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.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20530 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | DIuJg8eTDRgE |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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).
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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