Intelligence Density in Physical AI: Reduce the Problem Before Optimizing the Computer
Year: 2026
Type: preprint
Abstract: Artificial intelligence is becoming denser: equivalent capability can increasingly be achieved with fewer parameters, less memory and lower computational cost. For Physical AI, however, this is only the first of three efficiencies. A system may possess very large computational capacity, extract only a fraction of its possible useful intelligence from that capacity, and still devote more computation than the physical action actually requires. This Perspective argues that the first optimization should therefore occur before processor or model selection: reformulate the task so that only the information required for the next useful physical action must be computed. Nature repeatedly follows this strategy, from optic-flow control in insect flight to the vestibulo-ocular reflex and retinal preprocessing. Recent honeybee-inspired robot navigation provides an engineering example in which changing the representation of the navigation problem enables extremely compact computation. We propose embodied intelligence density as a family of system-level measures and position Reflex-Policy as one architecture for problem reduction, lowest-sufficient computation and selective escalation.
Source: Preprints.org
Author: Pietro Perlo
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7204131438