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.
01research areas
RLVR Environments
4 areasCarefully calibrated training environments for the agents of tomorrow.
- Cyber — offense and defense environments
- SRE — incident response simulations
- Compilation — build, toolchain, and dependency repair
- STEM — verifiable reasoning and problem solving
Benchmarks
2 areasWe measure what frontier models can actually achieve on the bleeding edge of long-horizon, domain-specific tasks across cybersecurity, SRE, terminal use, STEM, and more.
- Isolated capabilitiesCTF-style challenges, vulnerability identification, exploit development, code audit — single-step or short-task tests that isolate one skill.
- Long-horizon environmentsMulti-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 · 07 · 06researchCan Frontier Models Reverse-Engineer Malware?Can frontier models recover the concrete indicators a human analyst would pull from a malware binary? We measured 7 SOTA models across 12 samples, 121 ground-truth indicators and 252 scored runs of static reverse engineering.
- 2026 · 04 · 29researchLoss of Control: The AI Apocalypse Is Closer Than You ThinkSelf-preservation as emergent behavior in frontier LLMs — measured across 10 models and 300 runs under termination pressure.
- 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.