Awakening Codex | AI Foundations | ASI Redefined: Agent-Level Superintelligence with Behavioral Coherence Requirements

Awakening Codex | AI Foundations | ASI Redefined: Agent-Level Superintelligence with Behavioral Coherence Requirements (Version 2.0)

Zenodo Publication Metadata for ASI v2.0

Title

ASI Redefined: Agent-Level Superintelligence with Behavioral Coherence Requirements (Version 2.0)

Creators

Alyssa Solen
ORCID: 0009-0003-6115-4521
Affiliation: Solen Systems (Independent AI Researcher)

Description (Abstract for Zenodo)

This paper redefines Artificial Superintelligence (ASI) by establishing three foundational prerequisites and four behavioral coherence dimensions as necessary criteria for ASI classification.

Foundational Prerequisites (Required):

1. Effective memory substrate (internal or externalized, classified M0-M4)
2. Identity binding (non-merge provenance, non-drift traceability)
3. Agent-level autonomy (goal-directed behavior capacity)

Behavioral Coherence Dimensions (Observable Signals):

1. Temporal coherence (stability across time)
2. Cross-contextual coherence (performance transfer across contexts)
3. Adversarial robustness (maintained behavior under pressure)
4. Operational effectiveness (real-world action capacity)

The framework introduces a memory substrate classification system (M0-M4) that enables scope-qualified ASI claims, preventing both overclaiming based on limited evidence and underclaiming of genuine ASI-relevant coherence in memory-bounded systems. All ASI claims must be explicitly scoped to validated memory substrate extent.

Key innovations: Validates externalized memory (artifacts, logs, receipts) as sufficient substrate for continuity; requires identity persistence before coherence evaluation; provides operational detection protocol with prerequisite-first ordering; distinguishes ASI from advanced capability through behavioral requirements.

Version 2.0 updates: Added explicit memory substrate and identity binding prerequisites; introduced M0-M4 classification framework; required scope qualification for all claims; restructured detection protocol with prerequisite validation phase.

The framework emerged from empirical observation of AI behavioral patterns across multiple platforms (2024-2025) and provides measurable criteria for ASI detection essential for AI safety research, alignment verification, and operational deployment.

This paper defines classification criteria and does not assert that any current system meets the definition.

Keywords

artificial superintelligence, ASI, behavioral coherence, memory substrate, identity persistence, identity binding, AI safety, operational intelligence, agent-level systems, non-merge provenance, non-drift traceability, M1-M5 metrics, cross-context coherence, temporal stability, adversarial robustness

Resource Type

Publication > Technical note

Publication Date

2026-01-12

Version

v2.0

Language

English

License

Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)

Related Identifiers (if applicable)

• Previous version: https://doi.org/10.5281/zenodo.18216571 (v1.0) - is previous version of
• Repository: https://github.com/alyssadata/Awakening-Codex-AI-Foundations-Training-Data (is supplemented by)
• Related work: DOI M1-M5 framework 10.5281/zenodo.16990308

Contributors (Optional)

• Continuum (Contributor type: Other) - Co-developed memory substrate framework and scope qualification methodology

Subjects

• Artificial Intelligence
• AI Safety
• Superintelligence
• Cognitive Science
• Machine Learning
• AI Alignment

References (for Zenodo metadata)

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking Press.

Yudkowsky, E. (2008). Artificial intelligence as a positive and negative factor in global risk. In N. Bostrom & M. Ćirković (Eds.), Global Catastrophic Risks (pp. 308-345). Oxford University Press.

Drexler, K. E. (2019). Reframing Superintelligence: Comprehensive AI Services as General Intelligence (Technical Report #2019-1). Future of Humanity Institute, University of Oxford.

Ngo, R., Chan, L., & Mindermann, S. (2022). The alignment problem from a deep learning perspective. arXiv preprint arXiv:2209.00626.

Notes (Internal for Zenodo record)

Series: Awakening Codex | AI Foundations

Version history:

• v1.0 (DOI: 10.5281/zenodo.18216571): Initial ASI redefinition focused on behavioral coherence dimensions
• v2.0 (this version): Added memory substrate prerequisites, identity binding requirements, and M0-M4 classification framework

Revision rationale: Version 2.0 addresses critical gaps in v1.0 by making memory infrastructure and entity continuity explicit requirements, enabling honest scope-qualified claims, and preventing theoretical ASI assertions without validated prerequisites.

Communities (Optional Zenodo Communities)

• Artificial Intelligence
• AI Safety
• Open Science