[Emerging Ideas] Artificial Tripartite Intelligence: A Bio-Inspired, Sensor-First Architecture for Physical AI

TL;DR

This paper introduces Artificial Tripartite Intelligence (ATI), a bio-inspired, sensor-first architecture for physical AI that enhances accuracy and efficiency in dynamic environments by co-designing sensing and inference.

Contribution

The paper proposes a novel tripartite, modular architecture for physical AI, integrating sensor control, adaptive sensing, and reasoning within a closed-loop system.

Findings

ATI improves end-to-end accuracy from 53.8% to 88% in a mobile camera prototype.

ATI reduces remote inference invocations by 43.3%.

Sensor-adaptive sensing enhances performance in dynamic lighting and motion conditions.

Abstract

As AI moves from data centers to robots and wearables, scaling ever-larger models becomes insufficient. Physical AI operates under tight latency, energy, privacy, and reliability constraints, and its performance depends not only on model capacity but also on how signals are acquired through controllable sensors in dynamic environments. We present Artificial Tripartite Intelligence (ATI), a bio-inspired, sensor-first architectural contract for physical AI. ATI is tripartite at the systems level: a Brainstem (L1) provides reflexive safety and signal-integrity control, a Cerebellum (L2) performs continuous sensor calibration, and a Cerebral Inference Subsystem spanning L3/L4 supports routine skill selection and execution, coordination, and deep reasoning. This modular organization allows sensor control, adaptive sensing, edge-cloud execution, and foundation model reasoning to co-evolve within one closed-loop architecture, while keeping time-critical sensing and control on device and invoking higher-level inference only when needed. We instantiate ATI in a mobile camera prototype under dynamic lighting and motion. In our routed evaluation (L3-L4 split inference), compared to the default auto-exposure setting, ATI (L1/L2 adaptive sensing) improves end-to-end accuracy from 53.8% to 88% while reducing remote L4 invocations by 43.3%. These results show the value of co-designing sensing and inference for embodied AI.