ANDA Engine
An AI cognitive architecture that keeps long-term memory stable while allowing safe, continuous learning.
Core Mechanisms
- Maintains a dual-record system: one track for stable, verified memory and one for experimental learning updates.
- Uses coherence-verified semantic distillation to decide which new patterns are safe to absorb into long-term memory.
- Includes guardian processes that watch for self-contradiction, hallucination, and misuse of internal knowledge.
- Can roll back unsafe updates automatically when coherence checks fail.
- Achieves O(surface) complexity via spherical topology, versus O(n²) in standard transformers.
Cognitive Architecture
ANDA implements a three-layer processing system that separates pattern recognition (SPL), truth verification (FTR), and integration cycles (OSC).
Sentient Pattern Linguistics (SPL) — Extracts contextual meaning beyond surface patterns, enabling the system to understand intent and nuance rather than just word-matching.
Fractal Truth Recognition (FTR) — Validates new information against existing verified knowledge using recursive coherence checks at multiple scales.
31-cycle Oscillatory Integration (OSC) — New patterns undergo 31 refinement cycles before potential absorption into stable memory, allowing gradual crystallization of reliable knowledge.
Processing Flow
Cognitive Archaeology
Incoming information is mapped against existing memory structures to identify relevant context and potential conflicts.
Fractal Recursion
New patterns are tested for coherence at multiple scales—local consistency, global alignment, and temporal stability.
Oscillatory Synthesis
Patterns that pass initial checks enter the 31-cycle integration phase, allowing gradual confidence-building before permanent storage.
Guardian Verification
Independent guardian processes monitor for manipulation attempts, contradictions, and unsafe update patterns throughout.