Can it be used for multi-agent systems?

Yes, it includes a Multi-Agent Entropy Game framework that uses GF(3) roles (Generator, Coordinator, Validator) to reach Nash equilibrium through joint information gain.

How does temperature-aware sequencing work?

It uses Langevin dynamics where low temperature favors the exploitation of known high-information patterns, while high temperature encourages broader exploration of the interaction space.

What is the benefit of using this for training data?

By reordering interactions based on maximum entropy rather than chronology, models can often achieve up to 3x faster pattern learning by reducing redundant data processing.

What is the primary goal of the entropy-sequencer?

The primary goal is to arrange interaction sequences to maximize learning efficiency. It reorders interactions so that each step provides the highest possible information gain compared to the previous context.

What databases does it support?

The skill includes native DuckDB integration for storing, querying, and exporting entropy-optimized sequences for training or analysis.

Entropy Sequencer

Name: Entropy Sequencer
Author: plurigrid

byplurigrid

•

データサイエンスとML

Optimizes interaction sequences using information theory and active inference to maximize learning efficiency and information gain.

概要

The entropy-sequencer is a sophisticated tool designed to reorder interaction sequences for maximum epistemic value. By implementing principles from Active Inference and Soatto's Actionable Information framework, it rearranges data streams or task logs to prioritize high-information content over simple chronological order. This 'Layer 5' capability allows for significantly faster pattern learning by factoring out nuisances and maximizing conditional entropy, making it an essential skill for complex data analysis, multi-agent strategy optimization, and training data preparation.

主な機能

Provides efficient permutation search algorithms including beam search and genetic heuristics.
2 GitHub stars
Implements temperature-aware sequencing using Langevin dynamics to balance exploration and exploitation.
Maximizes information gain through greedy entropy optimization and conditional entropy calculation.
Performs nuisance factorization to extract minimal sufficient statistics from complex observations.
Integrates compositional game theory for multi-agent strategy optimization and Nash equilibrium analysis.

ユースケース

Accelerating pattern recognition in machine learning datasets by reordering examples for maximum learning impact.
Analyzing security threats like Domain Generation Algorithms (DGA) through anomalous entropy signatures.
Optimizing multi-agent systems and game strategies using information-sharing coalitions and GF(3) roles.

概要

主な機能

Provides efficient permutation search algorithms including beam search and genetic heuristics.
2 GitHub stars
Implements temperature-aware sequencing using Langevin dynamics to balance exploration and exploitation.
Maximizes information gain through greedy entropy optimization and conditional entropy calculation.
Performs nuisance factorization to extract minimal sufficient statistics from complex observations.
Integrates compositional game theory for multi-agent strategy optimization and Nash equilibrium analysis.

ユースケース

Accelerating pattern recognition in machine learning datasets by reordering examples for maximum learning impact.
Analyzing security threats like Domain Generation Algorithms (DGA) through anomalous entropy signatures.
Optimizing multi-agent systems and game strategies using information-sharing coalitions and GF(3) roles.