How does this skill approximate an uncomputable value like K(x)?

While K(x) is theoretically uncomputable, this skill uses LLMs to generate the most concise Python code possible that outputs the target data, providing a practical upper bound.

What is the 'KoLMogorov-Test'?

It is an evaluation framework that uses compression efficiency—the ability to find short descriptions for data—as a benchmark for measuring an AI model's true understanding.

What is the primary goal of the Kolmogorov Compression skill?

It aims to measure the intelligence of an agent or the complexity of a dataset by finding the shortest program that can reproduce the given data.

Can this skill be used for code deobfuscation?

Yes. By treating deobfuscation as a compression problem, it helps transform complex, obfuscated code into its simplest functional equivalent.

Kolmogorov Complexity & Compression

Name: Kolmogorov Complexity & Compression
Author: plurigrid

byplurigrid

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데이터 과학 및 ML

Analyzes and approximates Kolmogorov complexity to evaluate intelligence, optimize code brevity, and measure informational density.

소개

The Kolmogorov Compression skill leverages algorithmic information theory to assess data complexity and model intelligence through the lens of program synthesis. By approximating the shortest possible program to represent a dataset, it enables developers and researchers to quantify informational structure, filter noise from signal, and explore the limits of inductive inference. This skill is particularly useful for tasks involving code deobfuscation, evaluating LLM performance via the KoLMogorov-Test, and understanding the deep connections between compression, prediction, and artificial intelligence.

주요 기능

Provides frameworks for noise filtering and signal validation
Assists in minimal description program generation for complex datasets
2 GitHub stars
Integrates theoretical concepts like Solomonoff Induction and Sutskever's Thesis
Calculates intelligence scores based on data-to-code compression ratios
Approximates Kolmogorov complexity (K(x)) via LLM-driven program synthesis

사용 사례

Quantifying the informational density and structure of datasets for research
Reducing complex or obfuscated code to its simplest functional representation
Evaluating the 'intelligence' of different AI models using the KoLMogorov-Test

소개

주요 기능

Provides frameworks for noise filtering and signal validation
Assists in minimal description program generation for complex datasets
2 GitHub stars
Integrates theoretical concepts like Solomonoff Induction and Sutskever's Thesis
Calculates intelligence scores based on data-to-code compression ratios
Approximates Kolmogorov complexity (K(x)) via LLM-driven program synthesis

사용 사례

Quantifying the informational density and structure of datasets for research
Reducing complex or obfuscated code to its simplest functional representation
Evaluating the 'intelligence' of different AI models using the KoLMogorov-Test