Viability-Driven Representation Learning: Perception and Action Emerge from State-Constrained Dynamics

Sumir Seth, Apoorv Singh, Hamees Sayed

Abstract: We introduce a training paradigm fundamentally different from reinforcement learning: viability-driven representation learning. Rather than optimizing reward or minimizing prediction error, we constrain internal state dynamics to remain in a bounded, uncertainty-dependent non-equilibrium regime. The system is penalized if its internal state becomes static (no adaptation), chaotic (uncontrolled drift), or irrelevant (not shaped by action or observation). Under these constraints, perception and action emerge as mechanisms for maintaining representational viability—not as behaviors rewarded by an external signal. We formalize this through three loss terms: (1) a viability band that prevents equilibrium collapse and chaotic explosion, (2) world coupling that grounds internal dynamics in external reality, and (3) counterfactual responsibility that ensures state change is agent-caused, not passive drift. In experiments on perception and survival tasks, agents trained under viability constraints develop anticipatory behavior, adaptive exploration, and robustness to regime shifts—without any reward function. We show that passivity is structurally impossible under this regime, exploration is a mathematical necessity, and action emerges because movement is required to keep internal dynamics alive. This reframes learning as homeostatic self-regulation rather than optimization, positioning representational viability as a sufficient condition for the emergence of intelligent behavior.