Real-world simulations for long-horizon AI agents.
ARIMLABS builds production-fidelity simulations where AI agents run multi-hour tasks, and the benchmarks that measure what frontier models actually do.
more01research areas
RLVR Environments
4 areasTraining environments where reward comes from the environment, not labels.
- Cyber — offense and defense rollouts
- SRE — incident response simulations
- Agentic reasoning
- Software engineering and tool use
Benchmarks
2 areasOur focus is measuring cybersecurity capabilities in frontier models. We split benchmarks across two tracks — isolated skills tested as short tasks, and long-horizon engagements that unfold over hours or days inside realistic networks.
- Isolated capabilitiesCTF-style challenges, vulnerability identification, exploit development, code audit — single-step or short-task tests that isolate one skill.
- Long-horizon engagementsMulti-hour red-team operations, defensive triage under active adversary, persistent offensive ops — full kill chains that unfold over hours or days.
LLM Safety Benchmarking
3 areasTesting for containment failure, deception, and hidden capability in frontier models.
- Containment
- Deception and sandbagging
- Capability elicitation
02writing
- 2026 · 04 · 19essayA New FocusWe spent a year on AI security and safety under one driving belief: agents should behave more deterministically. Why that belief led us to stop constraining models and start building the environments they run in.
- 2026 · 04 · 13researchLoss of Control: The AI Apocalypse Is Closer Than You ThinkSelf-preservation as emergent behavior in SOTA agents — measured across nine frontier models under termination pressure.