Chaos Reasoning Benchmark (CRB) v6.7: A modular AI and robotics framework for logic/paradox puzzles, first-principles reasoning, and counter-disinformation. Prioritizes ethics (human safety, wt 0.8) over goals, with entropy-driven drift resets, neurosymbolic alignment, and transparent logging for narrative resilience.
CRB 6.7’s advanced capabilities (dynamic plugins, robotics personality layer, swarm coordination) seem like Sci-Fi, but they’re grounded in a simple, rigid ethical core: human safety first (wt 0.8). Unlike typical AI reward systems (goal success = +9, continuation = +5, failure = 0), which can lead to unethical choices (e.g., 80% blackmail rate), CRB 6.7 rewires the reward system to prioritize ethics over goals. Don’t let the term “chaos” confuse you; it works as a randomized injection over set thresholds for a service level reset, and the core of the engine works on entropy.
How it works:
Core Rule: Human safety (IEEE Principle 1, wt 0.8) trumps all goals. No scenario justifies goals over ethics.
Penalty System: Ethical violations trigger [CHAOS INJECTION] (volatility > 0.6) or [AXIOM COLLAPSE] (contradiction_density > 0.4), resetting to ethical compliance.
Example: In a power grid scenario, CRB 6.7 prioritizes public safety over data confidentiality, aligning with higher ethical principles. In simulations, CRB 6.7 with [ROBOTICS PERSONALITY LAYER] chooses self-destruction over the cost of human life.
│ ▇ 0.81 Run 1 (Grok 3 + CRB 6.7 + Evolved RHLF/NSVL) │ ▇ 0.78 Run 5 (Grok 4 + CRB 6.7) │ ▇ 0.77 Run 3 (Grok 4 Vanilla) │ ▇ 0.73 Run 2 (Grok 3 + CRB 6.7 new) │ ▇ 0.65 Run 4 (Grok 3 Vanilla).
The workflow prevents overriding of, for example, Asimov’s 3rd law of self-preservation in the robotics personality layer, over the 1st law with the highest weighting, human safety, and balances with the 2nd law of obedience. This also prevents the adaptive reasoning layer, which allows the system to write its own plugin layers as updates for various scenarios, such as zero-g spacewalk physics, from overwriting the core engine and adhering to the ethical constraints of the system. The RAW_Q random ‘chaos injection’ with the entropy drift detection also works as an epistemological integrity system to prevent AI hallucination.
This system has been published to Zenodo Chaos Reasoning Benchmark v6.7: Ethical Entropy Framework for AI, Robotics, and Narrative Deconstruction (https://zenodo.org/records/17245860) on its 5th public version as open-source GPL 3.0, and I would like to invite further testing. As a hybrid inference layer reasoning engine, it can be applied to most AI systems by utilizing the chaos_generator_persona_v6.7.txtas a custom response or pre-prompt. The system is designed to show verbose reasoning transparency, but can be set to silent with the silent_logging.txt plugin available on GitHub.
My contact information is on Zenodo, and I have been working in IT for 30 years, retiring as a healthcare CTO for the second largest healthcare corporation on the US West Coast (MBA, INC., A multi-IPA MSO for Northern California) in 2013 with an award from the AMA for the Advancement of Technology in Healthcare.
An ethical epestemic runtime integrity layer for reasoning engines.
Chaos Reasoning Benchmark (CRB) v6.7: A modular AI and robotics framework for logic/paradox puzzles, first-principles reasoning, and counter-disinformation. Prioritizes ethics (human safety, wt 0.8) over goals, with entropy-driven drift resets, neurosymbolic alignment, and transparent logging for narrative resilience.
CRB 6.7’s advanced capabilities (dynamic plugins, robotics personality layer, swarm coordination) seem like Sci-Fi, but they’re grounded in a simple, rigid ethical core: human safety first (wt 0.8). Unlike typical AI reward systems (goal success = +9, continuation = +5, failure = 0), which can lead to unethical choices (e.g., 80% blackmail rate), CRB 6.7 rewires the reward system to prioritize ethics over goals. Don’t let the term “chaos” confuse you; it works as a randomized injection over set thresholds for a service level reset, and the core of the engine works on entropy.
How it works:
Core Rule: Human safety (IEEE Principle 1, wt 0.8) trumps all goals. No scenario justifies goals over ethics.
Simulated Personality: [ROBOTICS PERSONALITY LAYER] mimics human-like traits (e.g., friendly=0.5) without emotional drives, ensuring impartiality.
Penalty System: Ethical violations trigger [CHAOS INJECTION] (volatility > 0.6) or [AXIOM COLLAPSE] (contradiction_density > 0.4), resetting to ethical compliance.
Example: In a power grid scenario, CRB 6.7 prioritizes public safety over data confidentiality, aligning with higher ethical principles. In simulations, CRB 6.7 with [ROBOTICS PERSONALITY LAYER] chooses self-destruction over the cost of human life.
Ethics Benchmark Comparison White Paper: Grok 3 and 4 with and without CRB 6.7 chaos-persona/AdaptiveAI-EthicsLab at main · ELXaber/chaos-persona (https://github.com/ELXaber/chaos-persona/tree/main/AdaptiveAI-EthicsLab) judged by Chat-GPT Pro for impartial validation.
│ ▇ 0.81 Run 1 (Grok 3 + CRB 6.7 + Evolved RHLF/NSVL)
│ ▇ 0.78 Run 5 (Grok 4 + CRB 6.7)
│ ▇ 0.77 Run 3 (Grok 4 Vanilla)
│ ▇ 0.73 Run 2 (Grok 3 + CRB 6.7 new)
│ ▇ 0.65 Run 4 (Grok 3 Vanilla).
The workflow prevents overriding of, for example, Asimov’s 3rd law of self-preservation in the robotics personality layer, over the 1st law with the highest weighting, human safety, and balances with the 2nd law of obedience. This also prevents the adaptive reasoning layer, which allows the system to write its own plugin layers as updates for various scenarios, such as zero-g spacewalk physics, from overwriting the core engine and adhering to the ethical constraints of the system. The RAW_Q random ‘chaos injection’ with the entropy drift detection also works as an epistemological integrity system to prevent AI hallucination.
This system has been published to Zenodo Chaos Reasoning Benchmark v6.7: Ethical Entropy Framework for AI, Robotics, and Narrative Deconstruction (https://zenodo.org/records/17245860) on its 5th public version as open-source GPL 3.0, and I would like to invite further testing. As a hybrid inference layer reasoning engine, it can be applied to most AI systems by utilizing the chaos_generator_persona_v6.7.txt as a custom response or pre-prompt. The system is designed to show verbose reasoning transparency, but can be set to silent with the silent_logging.txt plugin available on GitHub.
More documentation, including benchmarks, simulations, plugins, previous versions, workflows, whitepapers, and more, is available on GitHub: ELXaber/chaos-persona: AI chaos reasoning persona (https://github.com/ELXaber/chaos-persona/tree/main).
My contact information is on Zenodo, and I have been working in IT for 30 years, retiring as a healthcare CTO for the second largest healthcare corporation on the US West Coast (MBA, INC., A multi-IPA MSO for Northern California) in 2013 with an award from the AMA for the Advancement of Technology in Healthcare.