EMOTION VECTOR MEMORY 2.1 (EVM)

Emotion Vector Memory (EVM) v2.1 is a model-agnostic identity and interaction telemetry standard designed for long-term AI systems, conversational agents, and multi-LLM environments. The specification defines a directed vector–based interaction ontology in which each interaction generates a normalized displacement segment (Emotion Vector, EV) within a bounded multidimensional orientation space. Persistent identity trajectories are represented through a dual-track model separating human interaction orientation (PEV) and AI entity behavioral trajectory (EEV), constrained by a Fundamental Envelope Vector (FEV) ensuring bounded and recoverable identity evolution.

Version 2.1 preserves the closed core ontology introduced in v2.0 and introduces normative implementation extensions enabling deterministic reconstruction, cross-system identity portability, canonical export via the EVM Canonical Identity Snapshot (CIS), and interoperable telemetry logging independent of underlying model architectures. The standard functions as a storage-agnostic identity continuity layer that can be deployed without modifying model weights or inference pipelines.

EVM defines a minimal directed interaction-vector ontology together with deterministic logging requirements, boundary-recovery mechanisms, and interoperable export structures intended for research, enterprise AI identity systems, cross-agent coordination frameworks, and long-term conversational continuity architectures.

GitHub Reference Implementation

A structured reference repository is available at:

https://github.com/krehlikszabolcs/evm-emotion-vector-memory

The repository includes:

– structured repository layout
– minimal Python reference implementation
– Canonical Identity Snapshot (CIS) schema examples
– integration quickstart documentation
– governance and licensing framework

The Zenodo DOI release remains the canonical normative specification. The GitHub repository provides visibility and reference implementation support for integrators and researchers.

Keywords:
AI identity continuity, interaction telemetry, vector memory, AI memory architecture, conversational AI identity, model-agnostic memory layer, cross-LLM interoperability, identity trajectory modeling

License: CC BY-NC-ND 4.0 (non-commercial use permitted; commercial implementation rights reserved).
Patent status: Filing in preparation / Patent pending.

Author: Szabolcs Krehlik
ORCID: 0009-0003-8623-7876
Contact: szabolcs.krehlik@gmail.com