ANIMA: Artificial Neuro-Identity Memory Architecture for Cognitive Stability in AI Systems

This work introduces ANIMA (Artificial Neuro-Identity Memory Architecture), a conceptual cognitive architecture designed to enhance reasoning stability, identity continuity, and hallucination resistance in artificial intelligence systems.The framework explores how structured identity memory layers can guide logical reasoning and reduce instability in large language models.A supplementary validation report evaluates conceptual relationships between ANIMA and CSLV (Cross-Source Logical Validation), a reasoning validation mechanism aimed at detecting source-induced bias in AI outputs.Together, these frameworks contribute toward the development of more transparent, stable, and cognitively grounded AI reasoning systems.