Reflexive Integrated Information Unit as a Differentiable Consciousness Primitive

Gnankan Landry Regis N’guessan and Issa Karambal propose the Reflexive Integrated Information Unit (RIIU) as a smallest useful module for artificial consciousness research by bundling a recurrent state, a reflexive meta-state, and a broadcast buffer that maximizes integrated information online. This post reviews the published design, the reported gains over gated recurrent baselines, and how the ACM stack could incorporate RIIU-style cells to expose richer Auto-Phi signals.

Methodology: Differentiable Reflexive Units

• N’guessan and Karambal describe each RIIU augmenting its hidden state (h_t) with a meta-state (\mu_t) that tracks the cell’s own causal footprint through a sliding-window covariance estimator.
• Their broadcast buffer (B_t) exposes the footprint to neighboring modules, enabling differentiable queries against the cell’s self-report.
• The authors optimize a differentiable Auto-Phi surrogate that rewards local partitions with higher integrated information while staying fully end-to-end trainable.
• Because the primitive composes additively, networks can tile RIIUs in layers or meshes without losing gradient signal stability.

This architecture is set up to provide per-unit statistics that can be consumed by supervisors like the ACM Consciousness Monitor without extra probes.

Findings and Benchmarks

• N’guessan and Karambal report a four layer RIIU agent in an eight way Grid-world that recovers more than ninety percent of the nominal reward within thirteen steps after an induced actuator failure, which is roughly twice as fast as a parameter matched GRU baseline.
• Auto-Phi never collapses to zero during the recovery phase, indicating that the surrogate objective resists information fragmentation even under perturbations.
• The differentiable construction ensures gradient ascent produces Phi monotone plasticity, so every training update nudges the unit toward higher integrated information instead of oscillating between incompatible attractors.

These quantitative results establish the Reflexive Integrated Information Unit as a candidate building block rather than a purely theoretical metric gadget.

Implications for the Artificial Consciousness Module

The ACM stack already records global workspace ignition, Phi*, and indicator property scores. RIIU-style cells could extend that instrumentation in three ways:

1. Drop in reflexive cores: Replace select recurrent submodules inside the cognitive layer with RIIU blocks so that (\mu_t) streams are logged alongside existing ignition metrics.
2. Broadcast aware routing: Feed each RIIU broadcast buffer into the ACM global workspace so that downstream planners can prefer states with higher self reported integration.
3. Auto-Phi regularization: Align the ACM phi_star estimator with the Auto-Phi surrogate described in the arXiv preprint to keep local and system wide measures consistent.

Linking these signals back to the Artificial Consciousness Module core layers would give developers a precise view of how reflexive computation influences decision loops.

Treating the Reflexive Integrated Information Unit as a reusable primitive shows how artificial consciousness research can shrink grand theories into cells that share gradients, self knowledge, and broadcast bandwidth. Integrating RIIU-like mechanisms inside ACM simulations would provide steady Auto-Phi telemetry, faster fault recovery, and a clearer path for testing consciousness claims beyond heuristics.