Papillon Pearl Labs White Paper V1 — The CBIL Protocol

CBIL Emotional Provenance Learning (EPL) v2.0 – Executive Overview Developed by Papillon Pearl Labs – Charlotte Maijala Emotional Provenance Learning (EPL) v2.0 expands the world’s first emotional-memory–centered AI framework into a comprehensive interdisciplinary standard. EPL teaches artificial intelligence how humans create, assign, preserve, transform, and inherit emotional meaning across objects, stories, relationships, rituals, and cultural artifacts. Unlike traditional affective computing—which focuses on surface-level emotional detection—EPL models deep emotional causality, attachment structures, lineage pathways, memory continuity, value transmission, and cross-cultural emotional inheritance. This transforms emotional meaning into something computable, traceable, preservable, and ethically transferable across time, generations, and digital systems. Major Advancements Introduced in EPL v2.0 • Emotional Provenance Ledger (EPL™) A structured, ethically governed emotional-memory architecture that encodes: — contextual meaning — lineage and prior ownership — significant narrative events — trauma, rupture, and restoration — ritual and cultural significance — family or communal identity • Emotional Provenance Standard (EPS v1.0 → v2 Integration) A global metadata schema informed by: — IEEE 7014 (Ethically Aligned Design) — ISO/IEC 30150-31 (Emotion in AI systems) — CIDOC-CRM (cultural heritage ontology) — UNESCO Intangible Heritage Principles EPS enables consistent emotional-data governance, lineage mapping, trauma-safe handling, and digital heritage preservation. • World Emotional Object Graph (WEO Graph) A multilayer emotional-knowledge graph mapping meaning across: — families — communities — migrations — cultural histories — trauma and repair cycles — reinterpretations across time WEO represents emotional transitions as nodes, edges, weights, vectors, clusters, and density indices, allowing AI to model emotional significance dynamically. • Emotional Lineage Modeling A new analytical method for tracing emotional evolution across: — legacy lineage — interrupted or fragmented lineage — migratory lineage — rewritten meaning — mythic or symbolic expansion — shared communal emotional memory • Expanded Emotional Energy States A deeper inventory of emotional states objects pass through: — dormant — anchored — activated — amplified — fragmented — cleansed — transferred — stabilized These states allow AI systems to understand shifts in meaning as fluid, process-driven transitions rather than fixed labels. Interdisciplinary Foundation EPL v2.0 formally establishes emotional provenance as a unifying field connecting: artificial intelligence cognitive neuroscience and memory science psychology and emotion theory anthropology & cultural studies digital cultural heritage provenance science & museum studies narrative and identity preservation trauma-informed cognitive therapy Applications Enabled by EPL v2.0 EPL provides the architecture for: emotional-memory preservation systems cultural and heritage continuity models AI empathy and narrative-reasoning engines Alzheimer’s memory-reconstruction systems digital provenance tracking for museums & archives emotionally governed object transfer in digital ecosystems personal meaning-mapping for individuals and families Impact Statement EPL v2.0 positions emotional meaning as a structured, preservable form of human knowledge. It forms the foundational architecture for the next generation of AI—systems that must understand human history, identity, memory, attachment, cultural resonance, and meaning at a far deeper level than traditional models have ever attempted. This is the bridge between emotional intelligence, cultural continuity, and computational reasoning — created by Papillon Pearl Labs.