Our research.
Applied research at Rkive spans four directions — continuity models for temporal reasoning, AIR for autonomous experimentation, meta models for predictive search, and edge models for on-device inference. Methods developed here ship directly into Studio, Comms, HQ, and Base.
AIR
Autonomous Intelligent Research
Autonomous Intelligent Research for agentic neural architecture search (NAS). AIR runs memory-backed sweeps with intelligent steering, capturing structured traces from each cycle that become reusable signal for follow-on search and meta-model training.
Continuity Models
Temporal and logical continuity
Reliable reasoning across time, state, and workflow — temporal continuity as the mechanism, logical continuity as the result. Persistent context across editing sessions, publishing chains, and multi-step decisions in Studio.
Meta Models
Per-category effectiveness prediction
Category-specific surrogate models trained on structured AIR experiment data. They predict which architectures, runtime functions, and training techniques will perform well under specific constraints — turning experimental signal into a predictive search policy.
Edge Models
Multimodal on-device inference
Training and compression for powerful multimodal models under real device constraints — latency, memory, thermals, power. The goal is multimodal capability that remains useful once hardware limits are first-class constraints.
Collaborate
We work with researchers, engineers, and partners advancing reliable multimodal systems, agentic neural architecture search, and efficient on-device inference.
careers@rkiveai.com · partners@rkiveai.com