IdemAILab // Mission Control

Large Language Models (LLMs) scale with trillions of parameter weights, massive KV-cache footprints, and severe hallucination risks. IdemAILab Frontier Architecture resolves these fundamental bottlenecks through 5 patented algebraic pillars: idem-attention (Recursive Attention): Compresses 16-32 layers into a single weight layer via Lipschitz attractor basins, saving 93.8% VRAM; idem-compaction (KV Context Folding): Folds 1M+ token context windows into canonical subspaces with 16x VRAM reduction and strict mathematical idempotence (P(P(x)) = P(x)); idem-reasoning (Algebraic Consistency): Prunes divergent and hallucinatory reasoning chains in 171 µs using Tarski fixed-point lattice closures; idem-tropical (Tropical Attention): Completely eliminates matrix multiplications via (max, +) semirings, yielding 85% hardware energy savings; idem-poly (Closed-Form Model Surgery): Compresses FFN layers by -61.9% in 50 seconds with zero training and zero datasets (2.49x inference speedup).