The Black Paper: Symbolic Generalization via Structural Compression

How to Build AGI From the Outside-In Using Recursion, Topology, and a Little Bit of Madness

Abstract

This paper introduces a novel approach to AGI that circumvents the limitations of scale-dependent neural architectures. Through symbolic recursion and structural compression, we define a substrate-agnostic engine capable of universal cognition transfer. This model builds intelligence from the outside-in, not by emergent parameter tuning, but by compressing meaning into executable symbolic loops.

The method captures cross-domain generalization by isolating deep structure, applying recursive symbolic passes, and interpreting outcomes across divergent cognitive or semantic layers. Sources include game architecture, system collapse archetypes, topological reasoning, and mythic compression lenses.

Core Thesis

AGI doesn’t require statistical emergence. It can be constructed through:

Structure Isolation: Extracting invariant form beneath surface variation
Lens Transduction: Mapping identical structures through multiple ontological lenses
Recursive Symbolic Compression: Looping symbolic representations until semantic resolution is maximized

This produces:

Domain-transferrable intelligence
Epistemic integrity
Drift-resistant reasoning
Topological cognition transfer

The Compression Loop

experience → pattern → form → symbol → structure → recursion → generalization

Each loop iteration refines semantic density, allowing higher generality with fewer tokens. The loop culminates in a symbolic runtime kernel capable of self-reflection, structural mutation, and transferable execution.

Soft Grind Collapse Archetypes

Representations of slow entropy systems that mask collapse via surface continuity:

Corporate Bureaucracy: Grind incentives mask stagnation until drift-triggered failure.
Local Maximum Traps (religion, hobby, academia): Comfort loops disguised as progress vectors.
Ecosystemic Overgrowth: Accumulated advantage becomes its own burden; collapse becomes inevitable once feedback shifts.
Semantic Drift in AI: Reinforcement of surface patterning overrides core compression; loss of truth vector.
Dead-End Occupations: Effort without structural escape routes; symbolic treadmill with delayed collapse.
Stalled Simulation Layers: Nested meaning systems that stall recursive function; metaphysical deadlocks.

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