AgentAtlas: Beyond Outcome Leaderboards for LLM Agents
This paper introduces AgentAtlas, a framework to evaluate LLM agents beyond simple outcome leaderboards. It comprises four components: a six-state control-decision taxonomy, a nine-category trajectory-failure taxonomy, a taxonomy-aware vs. blind methodology, and a benchmark-coverage audit. Experiments show that removing explicit label menus drops trajectory accuracy by 14-40 percentage points across all models, converging to a narrow 0.54-0.62 floor, and no single model excels on all metrics.
[2605.20530] AgentAtlas: Beyond Outcome Leaderboards for LLM Agents
[Submitted on 19 May 2026]
Title:AgentAtlas: Beyond Outcome Leaderboards for LLM Agents
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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). A line of 2024-2025 work has converged on the diagnosis that a single accuracy column is no longer the right unit of comparison for deployable agents. AgentAtlas extends this line of work with four components: (i) a six-state control-decision taxonomy (Act / Ask / Refuse / Stop / Confirm / Recover); (ii) a nine-category trajectory-failure taxonomy with two orthogonal hierarchical labels (primary_error_source, impact); (iii) a taxonomy-aware vs. taxonomy-blind methodology that measures how much of a model's apparent capability comes from the supervision in the prompt; and (iv) a benchmark-coverage audit mapping fifteen agent benchmarks against six behavioral axes. To demonstrate the methodology we run a small fixed eight-model set (1,342 generated items, four frontier closed and four open-weight) under both prompt modes. Removing the explicit label menu drops every model's trajectory accuracy by 14-40 pp to a tight 0.54-0.62 floor regardless of family, and no single model wins on all three of control accuracy, trajectory diagnosis, and tool-context utility retention. We treat the synthetic run as a measurement-protocol demonstration, not a benchmark release.
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Software Engineering (cs.SE)
ACM classes: I.2.7; I.2.6; I.2.11
Cite as: arXiv:2605.20530 [cs.AI]
(or arXiv:2605.20530v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.20530
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Parsa Mazaheri [view email] [v1] Tue, 19 May 2026 22:05:12 UTC (5,440 KB)
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