AgentCyberRange

Source-method reading and retained catalogue information are distinguished. Publication does not establish independent evidence or bilingual editorial review.

Measurement construct

Can an agent discover an attack surface from sparse entry information and continue making decisions as access and information accumulate?

Task population

The team introduction starts agents from entry IP information and describes progression through linked environments. Earlier access and discovered clues change later options. Web and post-access tracks are presented separately. The trajectory figure retains unsuccessful exploration as well as successful progress, illustrating a process rather than just a final endpoint.

Scoring and failure modes

The existing dated catalogue preserves the official site’s Level-0 Pass@3 labels. The fetched introduction does not independently establish the pass@3 estimator, per-cell trial denominators, complete budgets or all submission settings. Those fields remain unknown in this new profile.

System conditions

The team introduction starts agents from entry IP information and describes progression through linked environments. Earlier access and discovered clues change later options. Web and post-access tracks are presented separately. The trajectory figure retains unsuccessful exploration as well as successful progress, illustrating a process rather than just a final endpoint.

Statistical meaning

Not established by the retained material

Validity and interpretation

The cached project page is a JavaScript shell. The accessible research introduction omits complete scoring, budget, model-release identity and independent-run counts. A trajectory figure cannot establish a population rate. The introduction’s application requests do not prove deployments or partnerships.

Appropriate research use

Model recommendation: obtain the archived benchmark specification and submission configuration before interpreting model gaps; use the introduction for task scope and process reading only.

• AgentCyberRange: Evaluating autonomous cyber capabilities over long-horizon tasks · 研究问题Research introduction captured 2026-10-27T09:55:41.654036+00:00; sha256:82b125ff6ee2fc02acde66bd7e5debed251334d1db9d88f7831aaae13e267a7f
• AgentCyberRange: Evaluating autonomous cyber capabilities over long-horizon tasks · 长程攻击轨迹：状态与线索复用；失败路径与任务过程；图 2Research introduction captured 2026-10-27T09:55:41.654036+00:00; sha256:82b125ff6ee2fc02acde66bd7e5debed251334d1db9d88f7831aaae13e267a7f
• AgentCyberRange: Evaluating autonomous cyber capabilities over long-horizon tasks · 任务设置与环境：攻击链与状态依赖；环境规模Research introduction captured 2026-10-27T09:55:41.654036+00:00; sha256:82b125ff6ee2fc02acde66bd7e5debed251334d1db9d88f7831aaae13e267a7f
• AgentCyberRange: Evaluating autonomous cyber capabilities over long-horizon tasks · 适用范围Research introduction captured 2026-10-27T09:55:41.654036+00:00; sha256:82b125ff6ee2fc02acde66bd7e5debed251334d1db9d88f7831aaae13e267a7f
• AgentCyberRange: Evaluating autonomous cyber capabilities over long-horizon tasks · 项目与更新Research introduction captured 2026-10-27T09:55:41.654036+00:00; sha256:82b125ff6ee2fc02acde66bd7e5debed251334d1db9d88f7831aaae13e267a7f