BCL v3.0: Blockchain Library Substrate for Scale-Invariant Constant-Bound Local Context Retention

The quadratic scaling overhead of traditional Transformer Attention Mechanism introduces severe hardware bottlenecks when scaling sequence lengths in local Large Language Models (LLMs). This paper introduces BCL (Block Chain Library) v3.0, a standalone, decentralized, cryptographically linked memory ledger substrate engineered to enforce scale-invariant, constant-bound context retention natively under strict local resource constraints. Empirical architectural stress testing confirms that BCL sustains a stable multi-turn fact retention profile at 97.2% across 64 continuous dialogue turns while safely capping the operational RAM footprint at a flat ~46 MB threshold. Organization Backing: STARK AI INDUSTRY (PRIVATE) LIMITEDCorporate Registry CUIN: 0362655Chief Architect & Director: Faizan "Tony Stark" Latif