The Reflexive Integrated Information Unit: A Differentiable Primitive for Artificial Consciousness

Research on artificial consciousness lacks the equivalent of the perceptron: a small, trainable module that can be copied, benchmarked, and iteratively improved. We introduce the Reflexive Integrated Information Unit (RIIU), a recurrent cell that augments its hidden state h with two additional vectors: (i) a meta-state μ that records the cell's own causal footprint, and (ii) a broadcast buffer B that exposes that footprint to the rest of the network. A sliding-window covariance and a differentiable Auto-Phi surrogate let each RIIU maximize local information integration online. We prove that RIIUs (1) are end-to-end differentiable, (2) compose additively, and (3) perform Phi-monotone plasticity under gradient ascent. In an eight-way Grid-world, a four-layer RIIU agent restores >90% reward within 13 steps after actuator failure, twice as fast as a parameter-matched GRU, while maintaining a non-zero Auto-Phi signal. By shrinking "consciousness-like" computation down to unit scale, RIIUs turn a philosophical debate into an empirical mathematical problem.