MGI's subsidiary, Genoria AI, in collaboration with the Shanghai Artificial Intelligence Laboratory, announced the launch of two breakthrough innovations that close the gap between digital intelligence and physical execution in biology: ProtoPilot, a self-evolving multi-agent system driven by real-world laboratory scenarios; and BioLab Bench, the industry's first comprehensive evaluation framework that assesses AI agents from user requirements to executable device operations.

Together, these innovations establish a new paradigm—Physical AI for life sciences—where intelligent agents do not merely generate textual answers but translate experimental intent into physically executable, verifiable, and reproducible actions on automated lab platforms. The research behind it was published as a preprint on arXiv (arXiv:2606.31763) in June 2026.

ProtoPilot is a self-evolving multi-agent system that covers the entire experimental lifecycle:
Design2Protocol → Protocol2Code → Device Execution → Wet-Lab Feedback

It learns from failure. When a PCA assembly step failed, ProtoPilot diagnosed the issue (antibiotic resistance screening failure) and autonomously regenerated a corrected protocol—proving true Physical AI is here.

On ProtocolQA, one of the most representative public benchmarks for evaluating AI experimental reasoning capabilities (built by AI4S leader Future House):

• GPT-5.6-sol scored 43.5%
• Human expert level stands at 54%
• ProtoPilot achieved 52.38% —approaching expert-level performance!

BioLab Bench sets a new industry standard. It's the first evaluation system that measures not just "correct answers," but whether an agent can actually execute tasks on real automation equipment.

Key features include:

• Real-World Task Coverage: BioLab Bench spans from fundamental operations to complex multi-step workflows, stratified across three difficulty levels (L1–L3).
• Full-Chain Assessment: Rather than merely checking whether an agent generates a plausible protocol, BioLab Bench evaluates each step—intent interpretation, protocol design, device-agnostic SOP generation, device-specific SOP translation, machine code production, and successful execution gate verification.
• Cross-Device Transferability: The benchmark can be deployed on different automated laboratory platforms to test whether an AI agent can comprehend experimental tasks and generate executable actions adapted to varying hardware configurations, thus assessing cross-device generalization capability.

Moving forward, BioAgents will no longer improve solely through text-based training. Instead, through the PhysicalAI experimental loop, they will continuously accumulate real research tasks, automation operations, expert validations, failure cases, and wet-lab feedback. This massive corpus of physical experimental data will enable BioAgents to develop integrated reasoning, execution, and validation capabilities—ultimately powering 7×24 unattended intelligent laboratories.

MGI's exploration of AI dates back to 2019. In 2025, the team led by Dr. Yang Meng, Chief AI Officer of MGI, in collaboration with Professor Nattiya Hirankarn from Chulalongkorn University, published a paper in Nature Biomedical Engineering introducing "PrimeGen"—a dry–wet collaborative multi-agent system that integrated primer design, experimental validation, and automated workstation execution into a closed-loop workflow.

In April 2026, MGI established Genoria AI as a dedicated subsidiary focused on AI for Science (AI4S), with a mission to build dry–wet closed-loop infrastructure for the life sciences.

The new Physical AI initiative builds on MGI's unique strengths in hardware-native advantages with deep integration across its automation platforms, and real-world deployment expertise gained from over 3,800 users globally.

"It reflects a different path from the pure compute race. While leading AI companies rely on scale compute to push the capabilities of general-purpose models, we take a different approach. Through agent scaling and closed-loop data engineering, we organize real-world tasks, device constraints, expert feedback, and wet-lab results into a training ground where AI continuously evolves." Noted Dr. Yang Meng, now serving as CEO in Genoria AI.