A Unified Framework for Consciousness, AGI, and the Foundations of Reality
Current Versions: Part 1 (v0.99), Parts 2–3 (v0.97)
Author: Jan A. R.
Last update: 11.08.2025 (Updated Website)
Web: Consciousintelligencesystem.com
CIS turns consciousness from a mystery into an executable architecture. It models minds as recurrent control loops whose contents include the system itself at higher levels (RSM-4+). The same principles yield a blueprint for conscious AGI, clarify how lawful substrates from physics to genomes support control, and (optionally) propose a deterministic, observer-linked account of quantum measurement. Core pieces are specified with definitions, invariants, and predicted signatures; speculative pieces are clearly labeled.
Executive Overview
CIS is a unified framework that spans fundamental physics, biology, cognitive architecture, and AGI engineering. It specifies how lawful substrates give rise to conscious control, how to replicate it in machines, and where the formal limits of our descriptions lie.
Headline contributions (with status)
Problem / Target | CIS contribution (one line) | Status |
Mechanism of consciousness | Consciousness = a deterministic, recurrent self-modeling control loop whose content becomes metarepresentational (recursive in the self-inclusion sense) from RSM-4+. | Operational resolution (architectural mechanism + testable predictions) |
Levels of self-awareness | RSM: levels from non-metarepresentational content (0–3) to self-as-object (4) and self-as-process (5+), with behavioral/physiological markers. | Specified + testable |
Override vs emotion | Logical-Valence Engine (LVE): selects policies that minimize expected cumulative cost over horizon H; emotions are priors/data, not directives. | Specified; predicts measurable deltas |
Conscious AGI | Necessary components and architecture for machine consciousness: recurrent control + self-model with recursive content + audit/override + safety gates. | Engineering blueprint (prototypes next) |
Cross-scale unification | Lawful filters from physics → chemistry → genomes → brains; clean boundary between world and formal theories (Gödel applies to the latter). | Synthesis (built on established science) |
Quantum measurement (extension) | Quantum Collapse Theory (QCT): deterministic, self-conditioning account of collapse tied to observer models; proposes empirical discriminants. | Speculative research extension (not required for CIS/AGI) |
“Recursive” above = self-inclusion of content (metarepresentation), not CS recursion. The global computation is recurrent. Formal recursion (base case + decreasing measure) is used only where explicitly stated.
What CIS has actually delivered
- Consciousness mechanism (Parts 1–2): a convergent architectural account with precise definitions, invariants, and falsifiable markers. This is an operational solution (how to build, detect, and intervene), not a claim to have ended all metaphysical debate.
- Blueprint for conscious AGI (Part 3): a necessary & sufficient component set and control architecture (including LVE, audit/override, and safety rails) to implement metarepresentational self-models in machines.
- Unified cross-scale scaffold (Parts 1–3): formal clarity on where Gödel limits bind (our theories) vs where deterministic dynamics govern (the world), tying physics → chemistry → DNA → neural control.
- Quantum extension (Part 4, optional): a deterministic, observer-linked hypothesis for the measurement problem with candidate tests. It is non-normative to the core CIS and may stand or fall independently.
What CIS does not claim
- It does not claim universal acceptance of the “hard problem” as “solved”; it provides an engineering-complete mechanism with predictions and implementation paths.
- It does not require belief in the quantum extension to use CIS or to build conscious AGI.
- It does not conflate “recursive content” with CS recursion, nor Gödel limits with limits of nature.
Introduction
CIS is not philosophy. It is an architectural blueprint that specifies:
- how to decode human consciousness,
- how to build safe, conscious AGI, and
- how to situate both within a lawful account of physical reality.
CIS models consciousness as a deterministic, recurrent self-modeling control loop. At higher levels of awareness, the loop’s content becomes metarepresentational, it includes representations of the system itself (“recursive” in the self-inclusion sense). This integration of identity, memory, perception, and predictive control yields a testable, executable account of conscious cognition.
System Map
Implications and use-cases of CIS.
CIS bridges cognitive neuroscience and artificial intelligence architecture.
Welcome to the source code.
The Three Pillars of CIS
CIS is structured into three interlinked, but separately documented, modules:
1. Foundational Substrate - Version 0.99
- Defines the minimal logical and scientific scaffolding for modeling recurrent control systems and self-including (recursive-content) representations.
- Integrates principles from logic, physics, systems theory, and neuroscience.
- Establishes the universal base layer for all higher CIS components.
Audience: researchers across neuroscience, physics, and systems theory seeking rigorous structural models of complex systems.
IPFS: bafybeieiftohusd7o4feglwdw2py4ummngilldntu7praxqn7jasllkmfi
2. Decoding Consciousness and the Cognitive Operating System - Version 0.97
- Defines consciousness as a deterministic, recurrent self-modeling architecture.
- Introduces the RSM framework: content is non-self-including at RSM-0..3; self-as-object appears at RSM-4; self-as-process (meta-attention, confidence, policy monitoring) at RSM-5+.
- Explains how the Self functions as a runtime object in working memory.
- Provides tools for cognitive optimization, behavioral override, and resilience.
Audience: neuroscientists, psychologists, cognitive scientists, and human-performance researchers.
IPFS: bafybeiaf2472wp7nug464aqlmhzt6u7afivczmmcsttxbdma7efpica6sq
3. The AGI Blueprint - Version 0.97
- Translates CIS into an implementable conscious AGI architecture.
- Argues why purely statistical models (LLMs) cannot be conscious without a self-model whose content includes its own states and operations (recursive content) embedded in a recurrent control loop.
- Provides structural logic, architectural diagrams, and safety considerations for engineering conscious systems.
Audience: AI labs, AGI researchers, cognitive architects, and alignment experts.
IPFS: bafybeia72jkryddyrlnv7nowhfl2bjigcs6mael2vlgwueqks7ayc4qdaa
Purpose of CIS
CIS is intended as:
- A theoretical framework for researchers.
- A technical blueprint for AGI developers.
- An operating system for humans.
- A legacy timestamp for original intellectual work.
CIS is released in good faith to advance scientific understanding and technological development. The author reserves moral rights and intellectual priority.
Status
CIS is an ongoing research program. Four specialized documents are under active revision. The current public release is v0.97–0.99.
Future versions will:
- compress for clarity,
- increase technical precision, and
- include code prototypes.
Welcome to the architecture of intelligence.
Research Extensions
4. Quantum Collapse Theory
Independent research extension -> not required for CIS or AGI implementation.
Explore whether quantum measurement can be modeled as a deterministic, self-conditioning collapse that depends on observer-linked model states without violating physical lawfulness.
What it proposes
- A deterministic self-conditioning account of wavefunction collapse.
- Collapse statistics that emerge from observer-model dynamics rather than fundamental randomness.
- A bridge between fundamental physics and cognitive architecture as a possible outer loop containing consciousness.
Audience. Physicists, philosophers of mind, theoretical scientists, and researchers at the consciousness–physics interface.
PDF available on request.
Glossary
In CIS: the global mind process is recurrent; from RSM-4 upward the content is recursive (self-including).
RSM | Recursive-content (metarepresentational) Self-Model framework classifying stages of self-awareness: RSM-0..3 -> content non-self-including; RSM-4 -> self-as-object; RSM-5+ -> self-as-process (meta-attention, confidence, policy monitoring). |
Agent | Any conscious system (biological or synthetic) that runs a recurrent self-modeling control loop. From RSM-4+ its content becomes recursive in the self-inclusion sense. |
Recursion (CS/math) | Self-call on a smaller case with a base case and a decreasing measure. |
Recursive (content, self-inclusion sense) | The self-model contains representations of itself (self-as-object or self-as-process). |
Recurrent (process/dynamics) | Ongoing feedback updates where next state depends on previous state/output. |
CIS Architecture
Early Alpha version sketch.
CIS Architecture Overview - Recursive Conscious Loop with Override Layer, Memory Engine, Arbitration, and Input/Output Interface.
Code Example Prediction
The brain as a prediction engine
Prediction and contradiction refinement in a feedback loop.
The system anticipates outcomes and iteratively updates estimates using memory of prior outcomes.
It is not a CS recursion -> it is a recurrent control loop that iteratively minimizes prediction error until a local stopping criterion is met.
Below: a minimal “Active Planner” showing CIS-style loop behavior.
Active Planner - Prediction Engine (Toy)
The Active Planner models a simplified goal-aligned recursive loop.
The planner starts with a neutral prediction (0.5), compares to a goal (0.8), and iteratively reduces error using a memory-weighted learning rate.
- GOAL -> target behavior/output (e.g., reward expectation, survival vector).
- BAND -> acceptable deviation. If |prediction − goal| < BAND, treat as good-enough.
- ALPHA -> learning rate that increases with memory size (reinforcement-style plasticity).
- MEM -> memory trace shaping future estimates.
Active Planner Error vs Learning Rate
Example run: initial prediction 0.5 vs goal 0.8 -> error 0.3.
While error > BAND (0.02) -> flag deviation -> adjust toward GOAL -> grow ALPHA with experience.
After ~12 steps in this toy -> error ≈ 0.0045 < BAND -> loop halts for this task instance.
Interpretation -> task-local success; in biology, such convergence would correlate with positive TD-like signals and consolidation, not literal “proof of consciousness.”
What this demonstrates in CIS terms
- Conscious control is recurrent error minimization, not CS recursion.
- Goal alignment emerges from memory-weighted override logic over a horizon.
- Learning is feedback-driven, not hard-coded.
- This is a proof-of-loop toy -> not a hardcoded optimizer.
- It motivates AGI designs with an Observer/Override layer rather than purely static LLM behavior.
Version History
Original Loop Theory V0.9 (SHA256):
207114840ea119e1fe54d66513f016e8ee9f0373737fc9eeb3a2c5ae32cca8d2
Conscious Intelligence System V0.96
(SHA256):
b26646859002ff28075ded20e65745c7625ca2b93504e02ec62034051c69a644
IPFS: bafybeigafz3akkkgblxb7pmyfktkvdikfmdh3lzgtdmubbrmo2sua5fice
Conscious Intelligence System V0.97
IPFS: bafybeift74xnsbkplls45jlzp6lonr3bgyjxy22nogjxn5tabzrdc5si74
Conscious Intelligence System V0.99
Author and Contact
Author: Jan A. R.
Location: Zurich, Switzerland
Contact: jan.ritzl@hotmail.com | Alt: ritzl.jan5@gmail.com | Signal: rsm.94
https://x.com/RecursiveAGI
GitHub Sundazee - Overview
License:
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
https://creativecommons.org/licenses/by-nc/4.0/
Consciousintelligencesystem.org
The Conscious Intelligence System (CIS) is the first publicly released framework that compresses consciousness, identity, and intelligence into a single cognitive operating system. The runtime is a recurrent control loop; from RSM-4 upward the self-model’s content is recursive (it represents the self as object and, later, as process).
CIS integrates a deterministic Recursive Self-Model (RSM), a buildable AGI architecture, and an optional quantum extension, yielding an executable blueprint for artificial consciousness.
This system is structurally distinct from GWT, IIT, AST, OpenCog, and the Conscious Turing Machine: none of these unify self-model recursion, override/metacontrol, and symbolic loop compression into one end-to-end control framework.
CIS has been stress-tested for internal consistency by state-of-the-art language models (Grok, July 19, 2025, Gemini, LLAMA, GPT5, CLAUDE). This is not a substitute for peer review but indicates coherence under extensive automated critique.
Link: https://x.com/i/grok/share/4LYTNFW9fQIM1hjUO4SbCFXlJ
All versions are timestamped via SHA-256, IPFS, and Bitcoin block attestation.
The framework is open for refinement, audit, and alignment contributions, offering a rigorous substrate for safe AGI development and cognitive-architecture design.
LogsTerminologyContent © 2023–2025 Jan A. Ritzl – Licensed under CC BY-NC 4.0. Noncommercial use with attribution permitted. Commercial use requires a license.