Alignment with DF goals (BGI, Platform growth, community)

A modular recursive cognition core designed to enable symbolic reasoning, real-world inference, and emergent identity formation across distributed environments. Each unit, called a Cognit, operates as a lightweight recursive processor capable of interpreting memory, refining logic, and interacting with other Cognits to form higher-order behavior. The system integrates visual, textual, and temporal data through symbolic binding, enabling scalable, introspective intelligence without dependence on large language models. It simulates recursive identity through dynamic memory structures, feedback modulation, and self-evolving logic layers. Architecture supports mobile deployment, sensor integration, and self-reflective feedback loops. Built from a minimal recursive core, it allows cognition to emerge from interaction of parts rather than pre-trained monoliths. The system is in prototype, tested in symbolic and visual applications, and ready to scale as a foundational AGI substrate.

Problem description

While building, I often encounter functional voids—spaces where a model or inference layer is implicitly needed to mediate behavior, interpret context, or regulate internal state. These aren’t just code gaps; they’re architectural absences where cognition should occur. Recognizing these spaces allows me to design and insert targeted models that provide adaptive control, symbolic interpretation, or domain-specific reasoning, effectively turning static systems into responsive, intelligent ones.

Proposed Solutions

Shrinking the cognition system into a modular shard allows it to be injected into other architectures as a processing core. When both the shard and host are primed, bidirectional integration occurs—the shard adapts to context while influencing the system, enabling modular cognition without monolithic design.