AIC-research is the academic and research arm of the Adaptive Intelligence Circle (AIC).
Its purpose is not to build the largest models, nor to chase short-term benchmarks. Instead, AIC-research exists to study, design, and verify intelligence systems that can adapt over long periods of time while remaining controllable, inspectable, and ethically grounded.
This repository serves as:
- A research laboratory
- A verification space for ideas implemented in AIC and IBCS
- A public academic record of assumptions, failures, and progress
- A question-driven research environment
- System-level AI research (not model-only)
- Focused on long-running, adaptive, and introspective systems
- Explicit about limitations, risks, and trade-offs
- A startup showcase
- A benchmark leaderboard project
- A place for hype-driven AI claims
- A shortcut to production deployment
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Control precedes scale Intelligence that cannot be controlled should not be scaled.
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Adaptation requires accountability Any system that changes itself must be able to explain, audit, and revert those changes.
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Runtime matters more than training Long-term behavior emerges during execution, not only during training.
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Human oversight is structural, not optional Humans are supervisors and governors, not just data providers.
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Negative results are first-class outcomes Failures are preserved, analyzed, and cited.
AIC-research currently investigates the following domains:
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Adaptive AI Systems Stability–plasticity trade-offs, continual adaptation, rollback mechanisms.
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Introspective Systems Self-modeling, behavioral tracing, introspection loops, and reflection engines.
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AI OS & Runtime Architecture AI as infrastructure, plugin-based intelligence, kernel vs user-space intelligence.
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Human-in-the-Loop Governance Consent, intervention thresholds, supervisory control.
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AI Safety & Control Trust boundaries, self-defense, kill-switch design, failure containment.
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Philosophy & Foundations of AI Responsibility, autonomy, and the ethics of adaptive systems.
Each research area is explicitly linked to corresponding code artifacts in AIC and IBCS.
AIC-research does not exist in isolation.
- AIC provides the system architecture and implementation context.
- IBCS (Introspective Behavioral Compiler System) provides experimental and runtime substrates.
Research ideas are expected to:
- Map to real system components
- Be validated through simulation or execution
- Acknowledge gaps between theory and implementation
AIC-research emphasizes:
- Clear problem formulation
- Explicit assumptions
- Reproducible experiments
- Long-duration system evaluation
- Ethical risk analysis
Accuracy alone is insufficient. Evaluation includes stability, explainability, recovery time, and human intervention cost.
All work in this repository adheres to:
- Proper citation and attribution
- Transparent reporting of limitations
- Careful handling of dual-use research
We avoid data that compromises privacy or safety and prioritize synthetic or simulated environments.
Contributions are welcome from researchers, engineers, and independent scholars.
However, all contributions are:
- Discussed before integration
- Reviewed on academic merit
- Evaluated for reproducibility and risk
See CONTRIBUTING.md and SECURITY.md for detailed policies.
AIC-research is designed as a long-horizon effort.
Its success is measured not by speed, but by:
- Conceptual clarity
- System robustness
- Intellectual honesty
- Usefulness to future researchers
If this work fails, its records should still help others avoid repeating the same mistakes.
This repository is an active research environment. Expect incomplete ideas, evolving designs, and open questions.
Stability is pursued at the system level, not at the surface level of documentation.
AIC-research is an independent open research initiative.
It is not affiliated with any corporation, government, or academic institution unless explicitly stated.
"Intelligence that adapts without reflection is not intelligence, but drift."