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AgentAtlas: Beyond Outcome Leaderboards for LLM Agents

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arXiv:2605. 20530v1 Announce Type: new 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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Authors: Parsa Mazaheri, Kasra Mazaheri
Released: 2026-05-22 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because AgentAtlas: Beyond Outcome Leaderboards for LLM Agents overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, under, eval, source, line, rate, control. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.20530

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