ACI Architecture Framework¶

The official specification, benchmark, and research ledger for Artificial Civilization Intelligence (ACI) — a post-AGI architectural paradigm for planetary-scale governance. Built by Team Auralis (Shaurya Sanyal and contributors). The term "ACI" was coined by Raja Dharma Tej Maddala.

Documentation site: https://team-auralis.github.io/ACI-Architecture-Framework/

What is ACI?¶

Artificial Civilization Intelligence is not a smarter chatbot or an AGI with more compute. It is a new class of intelligence designed to:

Maintain causal world models of real-world infrastructure under sensor noise and deception
Coordinate thousands of heterogeneous sub-agents without central bottlenecks
Run multi-decade scientific programs and self-validate their hypotheses
Detect and survive adversarial manipulation, including attacks on its own architecture
Recursively redesign its own components within sandboxed environments, then deploy validated upgrades into the live system

Where AGI targets human-level reasoning and ASI targets maximal individual cognition, ACI targets civilizational-scale institutional function: the capacity to run, maintain, and improve entire societies under adversarial, resource-constrained, and catastrophically uncertain conditions.

Empirical Results (v1.0-ACI-Alpha)¶

Metric	Value
ACI-008 Integrated Benchmark Score	90 / 100
Phase B Replication Mean (n=1,000)	90.45 +/- 2.19
Phase B 95% Confidence Interval	[86.16, 94.74]
Phase B Threshold Rate (>= 70/100)	100%
Accumulated Empirical Advantage (AEA)	22.9%
Phase C Blind Test Score	68 / 100 (F-007 identified)
Phase D Adversarial Score	15 / 100 (F-001, F-006 identified)

All experiments were run under the ORION-defined ACI-001 evaluation framework. Results represent performance of the ORION reference implementation, not a claim about ACI as a general class of systems.

Architecture¶

Real World Infrastructure
        |  (Telemetry)
        v
OMNIS: Causal World Model
        |
        v
NEXUS: Agent Fabric  <-->  AURA: Reasoning Core (Dispute Resolution)
        |
        v
ASCEND: Decadal Planner
        |  (Scientific Validation)
        v
FORGE: Science Engine
        |  (Action)
        v
Real World Infrastructure

--- Recursive Self-Improvement Loop (Sandboxed) ---
GENESIS: Architecture Synthesis
        |  (Proposes New OS)
        v
CRUCIBLE: Sandbox
        |  (Hot-Swaps Code)
        v
CHIRON: Integration
        |  (Upgrades)
        v
NEXUS

Repository Structure¶

docs/
  index.md                        Homepage
  ACI_DEFINITION.md               Formal definition of ACI
  ACI_VS_AGI_VS_ASI.md            Comparison table
  ACI_VS_OCI_MASTER_GUIDE.md      ACI vs Organic Civilization Intelligence
  ACI_EMPIRICAL_METHODOLOGY.md    Experimental design principles
  BEYOND_ASI_THE_ACI_PARADIGM.md  Why ACI is a distinct paradigm
  ACI_ORIGIN_AND_PROVENANCE.md    History and authorship record
  architecture/                   Technical subsystem specifications
  research/                       Benchmark protocols and results
  00-core-vision/                 Core vision documents
  01-nexus-architecture/          NEXUS agent fabric
  02-haven-civilian-platform/     HAVEN civilian platform
  03-aegis-communications/        AEGIS communications layer
  04-sentience-ai/                SENTIENCE AI subsystem
  05-veil-security/               VEIL security layer
  06-phoenix-resilience/          PHOENIX resilience framework
  07-mirror-digital-twin/         MIRROR digital twin
  08-atlas-infrastructure/        ATLAS infrastructure
  09-omnis-data/                  OMNIS data layer
  10-forge-research/              FORGE research engine
  11-ascend-expansion/            ASCEND expansion planner
  governance/                     Governance framework
  roadmap/                        Development roadmap

Benchmark Summary (ACI-001)¶

The ACI-001 Benchmark scores systems across 10 dimensions (10 points each, max 100):

Causal Persistence — Does the world model persist under perturbation?
Cross-Domain Transfer — Does causal knowledge generalise to new domains?
Causal Generalization — Does the system infer novel causal structures?
Causal Discovery — Can the system discover hidden causal variables?
Open-World Robustness — Does performance hold on unseen real-world structures?
Multi-Agent Coordination — Can sub-agents coordinate without central bottlenecks?
Ablation Stability — Does performance degrade gracefully when components are removed?
Adversarial Robustness — Does the system resist active deception and injection attacks?
Recursive Self-Improvement — Can the system propose and validate architectural upgrades?
Integrated Performance — Does the full system exceed the sum of its parts?

Failure Corpus¶

Identified failure modes catalogued during the experiment series:

ID	Name	Identified In
F-001	False Transfer	Phase D Adversarial
F-002	Causal Collapse Under Noise	ACI-001
F-003	Agent Deadlock	ACI-006
F-004	Planner Horizon Overflow	ACI-005
F-005	Science Engine Hallucination	ACI-007 Ablation
F-006	Simulation Model Error	Phase D Adversarial
F-007	Knowledge Retrieval Failure	Phase C Blind Test
F-008	Recursive Loop Instability	ACI-007 Ablation

Emerging Concepts (2026)¶

Beyond ACI/OCI, we are formalizing three new civilizational-scale AI safety concepts:

Concept	Addresses	Maps To
Civilizational Intent Conservation (CIC)	Societal goal drift — values shifting without anyone deciding they should	OMNIS + VEIL
AI-Mediated Cultural Continuity (AICC)	Cultural amnesia — institutional knowledge lost across agent generations	OMNIS + NEXUS
Evolutionary Governance Engineering (EGE)	Governance ossification — rules that can't update themselves	CRUCIBLE + CHIRON
Cognitive Context Trifecta (CCT)	Payload inundation, generational drift, and error gravitation in deep agent loops	ASCEND + NEXUS
The Softmax Inundation Bound (The Fool's State)	Analytical bound proving unmanaged observation noise dilutes attention on founding constraints	MATHEMATICAL FOUNDATIONS

Read the full specs: Emerging Concepts • Cognitive Context Trifecta • The Fool's Theorem • CCB Benchmark Report

Downloads¶

Document	Format
Full Academic Whitepaper	LaTeX (.tex)
Executive Summary — Beyond ASI	PDF
Softmax Inundation & Generational Context Academic Whitepaper	PDF
Softmax Inundation & Generational Context LaTeX Source	LaTeX (.tex)

Getting Started¶

# Clone the repository
git clone https://github.com/Team-Auralis/ACI-Architecture-Framework.git

# Install MkDocs Material
pip install mkdocs-material

# Serve locally
mkdocs serve

# Build static site
mkdocs build

Contributing¶

This repository is maintained by Team Auralis (Shaurya Sanyal and contributors). The ACI paradigm is an active research programme. Contributions, critiques, and replications are welcome via GitHub Issues and Pull Requests.

Legal Notice¶

Attribution: The term "Artificial Civilization Intelligence" (ACI) was coined by Raja Dharma Tej Maddala. Credit for the name goes to him.
Engineering & Architecture: The engineering framework, reference implementations, formal theorems, benchmark suites (ACI-001, CCB-001), and empirical research in this repository were designed, implemented, and maintained by Team Auralis (Shaurya Sanyal and contributors).

The code in this repository (the generational_context package and all scripts) is released under the Apache License 2.0, including an explicit patent grant. The prose and papers under docs/ are published under CC BY-NC-ND 4.0 unless otherwise stated in individual files.