Beyond Scale, Agents, and Embodiment: Governed Experience Compounding in EvoMind - An Evidence-Based Positioning of a Persistent Cognitive Runtime in the Emerging AGI Landscape

EvoMind is a local-first, governed cognitive runtime investigating whether deployment experience can become persistent, reusable, transferable operational capability.

Evaluating EvoMind against the contemporary artificial-intelligence landscape requires more than comparing parameter counts, language benchmarks, multimodal inputs, computer use, persistent memory, or robotic dexterity. Modern frontier systems already combine increasingly capable reasoning, multimodality, computer interaction, tool execution, persistent agent memory, and, in robotics, embodied reasoning and motor control.

This white paper therefore examines a narrower systems hypothesis: can a deployed cognitive architecture convert repeated success, successful recovery, and attributable correction into reusable operational capability while retaining evidence, governance, restart persistence, auditability, verification, and revocability?

The paper positions EvoMind as an experimental cognitive architecture organized around persistent memory, world modeling, reasoning, planning, governed execution, verification, learning, skill lifecycle management, and evidence-backed capability promotion.

It distinguishes runtime experience compounding from foundation-model training, conventional agent orchestration, and robotics; reviews currently available EvoMind evidence; identifies limitations that prevent a full AGI claim; and proposes a sealed longitudinal benchmark for testing causal experience-to-capability transfer.

The principal claim is deliberately bounded. EvoMind is not presented as demonstrated AGI, ASI, consciousness, or as superior to frontier AI laboratories. It is presented as a research system investigating whether governed experience after deployment can become persistent and transferable competence.

The proposed decisive experiment tests whether learned capability causes statistically significant improvement on strictly unseen related tasks, survives complete runtime restart, preserves prior competence, remains governable and revocable, and produces the expected performance loss under ablation.

Status: White paper / preprint; not peer reviewed.
Version: 1.0
DOI: 10.5281/zenodo.21881379