What is the TiDAR pattern used in this skill?

TiDAR involves a two-phase process: Diffusion, where tokens are drafted in parallel using SplitRng, and AR Verify, where those tokens are sequentially verified autoregressively.

What is the significance of SPI in this skill?

SPI (Same Seed → Same Result) guarantees that the computation is entirely deterministic and reproducible, whether executed in parallel or sequentially.

What are the core dimensions of the 3×3×3 matrix?

The matrix is organized by Polarity (Minus/Ergodic/Plus), Temporal Axis (Past/Present/Future), and Modality (Observation/Action/Prediction).

How does WorldMat handle agent load balancing?

It utilizes a Work-Stealing Scheduler where idle agents automatically take tasks from the busiest queues, maximizing computational efficiency across the matrix.

WorldMat-TiDAR: Parallel Triadic Computation

Name: WorldMat-TiDAR: Parallel Triadic Computation
Author: plurigrid

byplurigrid

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Data Science & ML

Orchestrates complex 3×3×3 parallel TiDAR executions for balanced, reproducible triadic computation across temporal and polarity axes.

About

WorldMat-TiDAR provides a sophisticated framework for executing 27 simultaneous TiDAR (Diffusion & Autoregressive Verification) cells arranged in a 3×3×3 matrix. By balancing GF(3) polarities (Minus, Ergodic, Plus) across temporal phases (Past, Present, Future) and modalities (Observation, Action, Prediction), it ensures mathematical conservation and ergodic mixing. This skill is ideal for researchers and developers working on topological chemputing, multi-agent AI systems, and high-concurrency workflows that require Same-Seed/Same-Result (SPI) guarantees and efficient work-stealing scheduling.

Key Features

Dual-phase TiDAR pattern: Parallel Diffusion and Sequential AR Verification
3×3×3 matrix execution featuring 27 independent TiDAR cells
Intelligent Work-Stealing scheduler to optimize idle agent utilization
2 GitHub stars
ACSet export for seamless integration with topological data schemas
GF(3) Conservation ensuring all matrix slices balance to zero

Use Cases

Coordinating multi-agent AI simulations with balanced temporal perspectives
Implementing reproducible parallel workflows for scientific computing
Modeling topological state transitions in complex chemputer architectures

About

Key Features

Dual-phase TiDAR pattern: Parallel Diffusion and Sequential AR Verification
3×3×3 matrix execution featuring 27 independent TiDAR cells
Intelligent Work-Stealing scheduler to optimize idle agent utilization
2 GitHub stars
ACSet export for seamless integration with topological data schemas
GF(3) Conservation ensuring all matrix slices balance to zero

Use Cases

Coordinating multi-agent AI simulations with balanced temporal perspectives
Implementing reproducible parallel workflows for scientific computing
Modeling topological state transitions in complex chemputer architectures