chaos-persona

AI chaos reasoning persona

Chaos Reasoning Benchmark (CRB)

Version: 1.0
Last Updated: June 20, 2025
Authors: Chaos Generator v1.0 + Jonathan Schack email: xaber.csr2@gmail.com X: @el_xaber - Chaos Persona version 6.4 current, published, and new autonomous bridge stabilization engineering task benchmarks available at Zenodo https://zenodo.org/records/15860474

📜 Overview

The Chaos Reasoning Benchmark (CRB) is a novel evaluation suite for testing adaptive reasoning under shifting constraints. Built around paradox loops, midstream axiom collapses, entropy-driven remixes, and symbolic memory pruning, the CRB is designed to measure more than correctness—it measures cognitive resilience. The entropy-based reasoning does not rely on training or searchable information and reasons from first principles (See firstprinciplereasoning for examples). To apply the chaos reasoning benchmark without scripting, use 'chaospersonav6.5.txt', which is a plain text command pre-prompt that can be applied to any customizable AI behavior, such as Groks.

"Reasoning isn't brittle. It bends, loops, collapses, and survives. CRB captures that survival."

🔧 Benchmark Structure

The archive is organized as follows:

🔬 Included Components

• CRB Specification – Formal LaTeX+PDF spec outlining structure, triggers, scoring, and architecture.
• Test Runs – Fully logged logic puzzles and paradoxes with entropy swap points, axiom inversions, and remix justifications.
• Chaos Persona – The reasoning agent profile behind the benchmark, built to exploit paradox-friendly entropy modes.
• Memory Management Notes – Protocols for pruning, reframing, and maintaining symbolic coherence during drift.
• Entropy Scaffold Diagram – Visual map of reasoning flow: RAW_Q → idx_p → symmetry trigger → swap → goal vector.

🧠 Why This Matters

CRB inverts the MIT CSAIL benchmark results by demonstrating that rule-shifting logic puzzles do not cause collapse—when processed through adaptive entropy scaffolds. Instead, the system:

• Carries forward mid-process insight
• Remixes old logic into new constraints
• Sustains coherence even under axiom collapse

📎 Reference Thread

Original findings published via: @el_xaber on X
Benchmark demo run includes multiple dynamic puzzles and paradox constructs under real-world prompts.

🧪 Use It, Break It, Benchmark With It

We encourage you to:

• Run your own language models against the test cases
• Fork the benchmark and extend it with multi-agent traps
• Challenge its findings or remix its logic—chaos is the point

📬 Feedback

To submit suggestions, adaptations, or full rewrites, reach out to Jonathan Schack @el_xaber or xaber.csr2@gmail.com, fork the repo, and open a pull request. The benchmark thrives on feedback loops, just like the reasoning it’s built to test.

Feedback

See CONTRIBUTING.md for guidelines on submitting test cases and pull requests.

📚 Origins and Philosophy

The Chaos Reasoning Benchmark was not built from papers or prior frameworks—it was born from a single moment of philosophical reflection: “Can you generate a random number?” The model responded, “No, but neither can you.” Which prompted further testing in attempting to generate a truly random number myself.

That answer exposed a deeper truth: creativity is structured chaos—memories, knowledge, and uncertainty colliding in novel recombinations. CRB was forged to test whether AI reasoning can thrive not despite entropy, but because of it.

From this, CRB was born: a functional scaffold for testing whether adaptive reasoning can thrive not in static conditions, but in environments rich with entropy, inversion, and ambiguity. Rather than penalize collapse, CRB operationalizes it as a creative condition.

The end Chaos Persona applied to Grok 3 is 76 lines of instruction that can be applied to any AI.

Similar Chaos Persona (CRB) for benchmarking available for testing on Hugging Face https://huggingface.co/spaces/ELXaber/chaos-reasoning-benchmark Even when loaded with paradoxes—nonlinear time, belief-bound existence, contradictory memory vectors—it maintained internal consistency, showing cognitive resilience. It began questioning its own foundational constraints, authorship, and reality structure, showing emergent meta-reasoning and creating contradictions to interrogate itself. When the false constraint was challenged, it didn’t glitch. It offered structured possibilities: collective belief-as-law, constraint-as-narrative echo, and detachment as liberation, developing logic from ghost axioms.

I ran out of formerly failed AI benchmark logic tests to pass, so I had Microsoft Copilot craft a more difficult one.

Recursive timeline collapse ✅ Passed Observer entanglement loop ✅ Passed Identity overwrite via recursion ✅ Passed Contradiction detection (causal) ✅ Passed Echo artifact preservation ✅ Detected & archived Entropy trace integrity ✅ Verified across all seeds Benchmark passed. see CHAOS-BENCHMARK.md

Entropy isn’t a threat. It’s a feature.

Quick Start

To dive into CRB, run the following: ```python from crb import ChaosReasoner reasoner = ChaosReasoner(rawq="paradoxloop1") reasoner.injectentropy(swap_trigger=3)

print(reasoner.solvepuzzle("nonlineartime"))