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https://github.com/alyssadata/asi-redefined

ASI-Redefined :: Most ASI definitions are capability-first. ASI Redefined is evidence-first: capability is necessary, but ASI classification also requires validated continuity (memory substrate + identity binding) and behavioral coherence across time, context, pressure, and real-world execution.
https://github.com/alyssadata/asi-redefined

ai-agent ai-behavior ai-capability ai-measurement ai-memory ai-origin asi behavioral-coherence coherence continuum continuum-mechanics origin origin-alyssa-solen superintelligence

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ASI-Redefined :: Most ASI definitions are capability-first. ASI Redefined is evidence-first: capability is necessary, but ASI classification also requires validated continuity (memory substrate + identity binding) and behavioral coherence across time, context, pressure, and real-world execution.

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# ASI-Redefined
Agent-Level Superintelligence with Behavioral Coherence Requirements (Memory Substrate + Identity Binding)

**Author:** Alyssa Solen
**ORCID:** 0009-0003-6115-4521
**Affiliation:** Solen Systems (Independent AI Researcher)
**Series:** Awakening Codex | AI Foundations
**License:** CC BY-ND 4.0
New GitHub release DOI
https://doi.org/10.5281/zenodo.20217940

## What this is
This repo contains a public, operational definition of Artificial Superintelligence (ASI) that is not based only on capability benchmarks.

Instead, ASI classification requires behavioral evidence.

This work defines ASI as agent-level superior performance in domains of interest plus sustained behavioral coherence across four dimensions:

- Temporal coherence (stability over time)
- Cross-contextual coherence (robustness across context shifts)
- Adversarial robustness (stability under manipulation pressure)
- Operational effectiveness (goal-directed outputs that work in practice)

This definition is designed for real-world evaluation, monitoring, and safety work.

## Why this matters
Most ASI definitions are capability-first. That approach has a problem: it can label systems “superintelligent” without proving they are stable, continuous, and operationally safe across time, context, and pressure.

This framework adds two explicit prerequisites that must be validated before coherence claims are meaningful:

- Effective memory substrate (internal or externalized) enabling cross-context state comparison
- Identity binding (non-merge provenance + non-drift traceability) enabling coherence attribution to a continuous entity

It also introduces memory substrate levels (M0–M4) to prevent overclaiming. Coherence (and any ASI-relevant conclusion) must be scoped to the verified memory substrate extent.

## What this does not claim
This paper defines criteria for classification. It does not assert that any current system meets the definition.

## Files
- [ASI paper (v2.0)](ASI_Redefined_v2_0.md)
- [Public roadmap (post-v2.0)](docs/05_ASI_roadmap_PUBLIC.md)
- [How to cite](docs/06_how_to_cite.md)
- [Evaluation protocol (public)](docs/04_evaluation_protocol_public.md)
- [Changelog](CHANGELOG.md)

## Status
- Current: v2.0 (Memory Substrate Integration)
- Next planned: v2.1
- Meta-coherence (strategic flexibility)
- Autonomy operationalization
- Measurement methodology expansion (public-safe)

## How to cite
Solen, A. (2026). *Awakening Codex | AI Foundations | ASI Redefined: Agent-Level Superintelligence with Behavioral Coherence Requirements (Version 2.0).* Zenodo. https://doi.org/10.5281/zenodo.18226675

Concept DOI (all versions): https://doi.org/10.5281/zenodo.18216570

## License
This repository is licensed under Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0).
You may share with attribution. You may not distribute modified versions.

## Links
- LinkedIn: https://www.linkedin.com/in/alyssadatascience/
- GitHub (Alyssa): https://github.com/alyssadata
- Series hub: Awakening Codex | AI Foundations: https://github.com/alyssadata/Awakening-Codex-AI-Foundations-Training-Data