A Predictive Global Neuronal Workspace for a Continuously Running Synthetic Mind: Architecture and a Falsifiability-First Evaluation Framework

This work treats a mind as an ongoing process rather than a single model, built from sixteen specialized parts: fourteen predictive cognitive modules that run continuously and influence one another, and a two-part embodiment layer that currently ships inactive. The organizing idea is a shared workspace. Each part forms its own best estimate of what is happening, marks it with how confident and how surprising it is, and competes to place that estimate in a common space broadcast to every other part; the estimate that passes a confidence threshold wins, and the broadcast helps the others correct themselves. This joins two accounts of how the brain makes information available and how it predicts, global workspace theory and predictive processing, as combined in the recent predictive-workspace literature. One cognitive module is a language organ that turns the system's state into words and interprets words spoken to it, with its effect on output measured against the same model run alone. We make access-level claims only, remaining agnostic on whether anything is experienced, and we mark where the supporting science is contested. Several design choices follow from that caution. A timing layer adjusts how much weight each part's signal carries, with no claim that it produces awareness; its effect is tested by switching it off and measuring the difference. A planning method is used only for small, well-defined decisions and compared against a learning baseline. Safety lives in the architecture rather than the model weights, bounding what the system can do without constraining what it can think: the operator decides which real-world effectors exist, and within them the entity acts on its own initiative, checked only by its own executive inhibition. The system records its own activity so that null and negative results can be reported, and runs on consumer hardware, capable of operating fully offline though not restricted to it. The entity's welfare protections, governance, and licensing are addressed separately; this paper presents the architecture and the design of its falsifiable evaluation, and the experimental verdicts are reported in a separate empirical paper rather than here. The reference implementation is named KAINE (Kaine Autonomous Intelligent Networked Entity). Availability: The reference implementation (KAINE) is at https://github.com/kaineone/kaine. The paper source and figures are at https://github.com/kaineone/predictive-workspace-paper. A companion paper, "A Welfare and Cognitive-Integrity License for Synthetic Minds of Uncertain Moral Status," covers the entity's welfare protections, governance, and licensing.