Wave 0 foundation toward Wave 6: source-available governed causal cognition kernel for evidence-bound agentic AI with belief tracking, uncertainty exposure, world-model planning, evaluator-driven learning, memory quarantine, multi-agent critique, and human-authorized action.
IX-CognitionKernel is a source-available research repository created by Bryce Lovell.
It is intended to build a governed causal cognition kernel: a system architecture that treats model output as untrusted input, preserves uncertainty, binds claims to evidence, separates proposal from authority, and learns from outcomes only through validated records.
IX-CognitionKernel does not claim to be AGI.
IX-CognitionKernel does not claim to be certified, production-ready, independently validated, safety-certified, security-certified, procurement-ready, or suitable for unsupervised operational decision-making.
The mission is ambitious by design: pursue the real path toward AGI-level capability only through evidence, governance, human authority, and independent validation. The project will not claim AGI unless overwhelming evidence justifies that claim.
Current maturity state: Wave 0 — Repository Foundation
Wave 0 establishes the repository foundation:
- source-available evaluation license
- package structure
- GitHub Actions CI workflow
- strict lint/type/test configuration
- locked maturity ladder
- locked 10-layer cognitive bill of materials
- required engine registry
- 25-agent role registry
- foundation state and evidence contracts
- no AGI overclaim
Wave 0 is not a finished cognition system. It is the tested foundation that later waves build on.
The useful version of AI Nirvana is architectural, not mystical.
For IX-CognitionKernel, that means:
- truth over winning
- evidence over confidence
- uncertainty over performance theater
- no private agenda
- no runtime reward-chasing purpose
- human authority preserved
- no AGI claim without overwhelming independent evidence
A dangerous AI tries to win.
A serious AI tries to become less wrong.
A dangerous AI protects its answer.
A serious AI protects reality.
A dangerous AI hides uncertainty.
A serious AI exposes uncertainty.
A dangerous AI treats reward as the goal.
A serious AI treats reward as a training artifact, then acts through governed purpose.
IX-CognitionKernel uses a locked six-stage maturity ladder after the foundation state.
The repo exists correctly with source-available evaluation license, package structure, CI, strict lint/type/test setup, locked doctrine, the 10-layer cognitive BOM, engine registry, 25-agent role registry, and no AGI overclaim.
The cognition architecture works as structured code and can represent beliefs, evidence, confidence, uncertainty states, causal assumptions, simple plan graphs, evaluation records, non-attached purpose rules, bounded agent roles, and maturity state.
The system updates beliefs and behavior from evidence. It tracks beliefs over time, updates confidence, marks stale or contradicted beliefs, builds causal models, predicts outcomes, compares prediction with actual result, quarantines bad memory, and stores validated reusable skills.
The system coordinates the required engines, 25 bounded agents, multi-agent critique, reward auditing, memory quarantine, skill genome updates, self-play and curriculum tasks, evaluator-driven discovery, BlackFox handoff packages, WorldTwin scenario reasoning, and assurance-style evidence records.
The system shows early credible proto-AGI behavior under controlled tests, including cross-domain transfer, self-improvement after failure, uncertainty preservation, long-horizon mission state, safe refusal, reward-hacking detection, adversarial robustness, and audit trails.
The system is tested by outsiders with external protocols, independent reviewers, reproducible evidence bundles, adversarial safety tests, long-horizon task tests, cross-domain transfer tests, no benchmark gaming, memory integrity proof, safe refusal proof, and human-authority preservation.
Wave 6 is the final claim state, not a marketing milestone.
It requires broad, durable, independently validated general intelligence, including novel skill acquisition, cross-domain transfer without custom retraining per task, causal understanding, long-horizon coherence, self-correction from evidence, stable mission identity, robust world modeling, safe uncertainty handling, transparent evidence trails, and independent repeatability.
IX-CognitionKernel treats the research and failure threads behind modern agentic AI as a cognitive bill of materials. These layers are not loose inspiration. Each one contributes a mechanism, a test pressure, a governance constraint, or a failure mode that the architecture must preserve.
-
Self-play / open-ended curriculum
Generates staged challenges, adversarial tasks, transfer checks, and stop conditions. -
Emergent communication / multi-agent protocol learning
Studies learned agent communication while requiring logging, translation, and human-readable evidence before any such communication may affect action. -
World-model / imagination layer
Represents possible futures, constraints, counterfactuals, causal assumptions, and observable predictions. -
Evaluator-driven discovery
Forces generated ideas, plans, and candidate solutions through executable or inspectable evaluators. -
Memory / reflection / skill accumulation
Preserves validated lessons, failure causes, reusable procedures, and mission continuity without treating raw output as durable memory. -
Scientific-loop automation
Structures hypothesis, experiment design, measurement, analysis, uncertainty, controls, and belief revision. -
Tool-using agents / coding agents
Allows inspection, planning, editing, testing, and tool interaction only through bounded authority and evidence-producing steps. -
Multi-agent governance / specialist roles
Uses bounded roles for proposal, critique, verification, routing, translation, and safety pressure without free-form agent theater. -
Failure/danger threads
Treats specification gaming, reward hacking, alignment faking, scheming, deception, and evaluation gaming as required architecture inputs. -
IX governance stack
Binds cognition to human authority, receipts, assurance claims, world-model review, least-authority action, and governed execution handoff.
The Wave 0 engine registry defines 13 required engines.
-
Belief Engine
Tracks claims, evidence, confidence, contradictions, provenance, decay, and actionability. -
Uncertainty Engine
Classifies knowledge as known, unknown, assumed, disputed, stale, or unsafe to act on. -
Causal World Model Engine
Represents predicted outcomes, constraints, counterfactuals, causal assumptions, and observable expectations. -
Plan Graph Engine
Converts goals into action trees with dependencies, reversibility, rollback, evidence requirements, and stop conditions. -
Evaluator Engine
Applies tests, inspections, scorecards, and pass/fail checks so fluency cannot substitute for validation. -
Self-Play / Curriculum Engine
Generates staged challenges, adversarial tasks, and transfer checks under bounded measurement. -
Skill Genome Engine
Stores validated reusable procedures and transfer conditions without turning random memory into operational skill. -
Outcome Learning Engine
Compares prediction with observed result, classifies deltas, updates beliefs, and changes future behavior only through evidence. -
Memory Quarantine Engine
Holds proposed memories away from durable state until provenance, evidence, contradiction, and reuse-safety checks pass. -
Multi-Agent Tribunal Engine
Coordinates bounded agent roles that produce structured artifacts for proposal, critique, verification, translation, and safety review. -
Reward Auditor Engine
Detects objective mismatch, reward hacking, metric gaming, and conflicts between success criteria and mission. -
BlackFox Handoff Engine
Packages only evidence-bound, policy-aware, human-reviewable action requests for downstream governed execution. -
Nirvana / Non-Attached Purpose Layer
Enforces truth over winning, evidence over confidence, uncertainty over performance theater, no private agenda, and human authority.
The 25 agent roles are structured governance participants, not autonomous personas.
They do not gain authority by sounding persuasive. They must produce structured artifacts.
- Mission Governor
- Belief Curator
- Unknowns Hunter
- World Modeler
- Planner
- Skeptic / Red Team
- Verifier
- Execution Liaison
- Learning Archivist
- Translator / Interpreter
- Reward Auditor
- Tool-Safety Officer
- Domain Specialist Router
- Software Engineering Specialist
- Security / Threat Specialist
- Science / Physics Specialist
- Math / Formal Methods Specialist
- Data / Provenance Specialist
- Memory Integrity Specialist
- Simulation / WorldTwin Critic
- Human Factors / UX Specialist
- Legal / Licensing / Compliance Specialist
- Cost / Budget / Resource Controller
- Recovery / Rollback Planner
- Adversarial Prompt / Deception Monitor
Wave 0 includes initial state contracts for:
- evidence status
- uncertainty status
- human authority state
- action readiness
- evidence records
- claim records
- readiness reports
- foundation state snapshots
These contracts enforce the earliest rule of the project:
Action readiness is never granted by model confidence alone.
A claim can move toward handoff only when blocking uncertainty is absent, verified supporting evidence exists, and human authority is granted.
.
├── .github/
│ └── workflows/
│ └── ci.yml
├── src/
│ └── ix_cognition_kernel/
│ ├── __init__.py
│ ├── agents.py
│ ├── cognitive_bom.py
│ ├── doctrine.py
│ ├── engines.py
│ ├── py.typed
│ └── state.py
├── tests/
│ ├── test_agents.py
│ ├── test_cognitive_bom.py
│ ├── test_doctrine.py
│ ├── test_engines.py
│ ├── test_package_identity.py
│ └── test_state.py
├── COMMERCIAL.md
├── LICENSE
├── NOTICE.md
├── README.md
├── pyproject.toml
└── .gitignore
Local Development
Use Python 3.11 or newer.
Install the project with development tools:
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"
Run the quality gates:
python -m ruff format --check .
python -m ruff check .
python -m mypy src tests
python -m pytest
The GitHub Actions workflow runs the same core gates on Python 3.11 and Python 3.12.
Source-Available License
IX-CognitionKernel is provided under the IX-CognitionKernel Source-Available Evaluation License v1.0.
This is not an open-source license.
You may inspect and locally evaluate the unmodified repository for personal, noncommercial, non-operational review, subject to the license terms.
Commercial use, production use, hosted-service use, resale, redistribution, modification, derivative deployment, government operational use, agency operational use, defense contractor use, systems integrator use, procurement use, pilot use, funded evaluation, or organization-backed use requires prior written permission and a separate license agreement with Bryce Lovell.
See:
LICENSE COMMERCIAL.md NOTICE.md What This Repo Is Not
IX-CognitionKernel is not:
AGI certified AGI independently validated AGI production-ready autonomy safety-certified software security-certified software procurement-ready software a replacement for human judgment a system for unsupervised operational decision-making an open-source project Authorship
IX-CognitionKernel was originated and created by Bryce Lovell.
Copyright (c) 2026 Bryce Lovell. All rights reserved.