VectorCertain LLC announced the commercial availability of its Micro-Recursive Model with Cascading Fusion System, an architecture designed to address AI safety vulnerabilities in mission-critical applications where traditional systems fail on rare edge cases. The technology deploys ensembles of ultra-compact models as small as 71 bytes each to provide safety coverage in statistical tails where catastrophic events occur.
"This is a transistor moment for AI safety," said Joseph Conroy, Founder and CEO of VectorCertain. "We're not improving existing AI architectures. We're enabling entirely new ones." The problem stems from commercial AI ensembles exhibiting cross-correlation exceeding 81%, meaning they fail on the same edge cases simultaneously, creating what Conroy calls "a false consensus that collapses precisely when you need it most."
The MRM-CFS architecture solves this through four interconnected innovations: micro-recursive models achieving >99% accuracy on target event categories despite being over 1 billion times smaller than GPT-4; overlapping sensor fusion ensuring no single sensor failure creates blind spots; a two-stage classification pipeline with governance escalation triggers; and a cascading fusion system that preserves minority opinions rather than simply voting.
VectorCertain has validated the architecture on multi-camera perception systems representative of autonomous vehicle applications. The system processes inputs from 8 cameras, detecting 6 tail event categories including pedestrian incursion and lane departure. The complete 256-model ensemble fits in approximately 20 KB of memory, achieves inference latency under 1 millisecond per frame, and delivers >99.2% accuracy on tail events in unseen test data.
A critical advantage is deployment on legacy hardware that cannot run modern deep learning models. Millions of embedded systems—automotive ECUs, medical devices, industrial controllers, and financial trading systems—operate on 8-bit and 16-bit processors with kilobytes of available memory. VectorCertain's 71-byte models enable full 256-model ensemble deployment across these constraints, achieving sub-millisecond latency with negligible power and thermal overhead.
The company is developing hardware integration that will redefine AI safety at the silicon level through a three-phase roadmap culminating in "Smart Gate" architecture where MRM functionality replaces traditional transistor logic at the gate level. "When your model fits in 71 bytes, you can bake it directly into routing tables," Conroy explained. "The transistor was passive. The Smart Gate is active. That's the paradigm shift."
The micro-footprint architecture enables mathematically provable fault tolerance through combinatorial redundancy. Where conventional frameworks require 640 KB for a 256-model ensemble, MRM-CFS deploys the same capability in 20 KB—a 32× memory advantage that enables every sensor to participate in multiple overlapping classifier groups. "We can mathematically prove there are no blind spots after single sensor failure," Conroy said.
VectorCertain's launch coincides with unprecedented regulatory pressure across multiple industries. The company's Safety & Governance System provides audit trails and human oversight mechanisms required by regulations including NHTSA's AV STEP Program, ISO 26262 ASIL-D, SEC compliance requirements, FDA frameworks, and NERC standards carrying penalties up to $1.25 million per day for AI affecting grid operations.
While autonomous vehicles represent a visible application, MRM-CFS applies wherever AI decisions carry high-consequence outcomes across more than 47 distinct application domains. These include medical diagnostics detecting rare conditions, financial trading identifying flash crash precursors, cybersecurity recognizing zero-day exploits, industrial safety predicting equipment failures, aviation verifying flight control decisions, energy grid detecting cascade failure patterns, pharmaceutical manufacturing ensuring batch quality, and surgical robotics validating control decisions.
"The combined addressable market exceeds $500 billion by 2030," Conroy said. "And that's before considering the installed base of legacy systems that can finally participate in AI safety advances." VectorCertain estimates $1.777 trillion in losses could have been prevented over 25 years if MRM-CFS had been available across trading losses, autonomous vehicle incidents, medical errors, and cybersecurity breaches where tail events defeated conventional AI.
VectorCertain's MRM-CFS architecture is available for enterprise licensing through www.vectorcertain.com.
