Global Workspace Competition + Broadcast + Working Memory with Ignition Dynamics

Tokenize and fuse multi-modal encoder outputs into a constrained global workspace.

Updated Feb 28, 2026
One-click install
npx skills add https://github.com/sovr610/refffiy --skill global-workspace-competition-broadcast-working-memory-with-ignition-dynamics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Global Workspace Competition + Broadcast + Working Memory with Ignition Dynamics
Source: https://github.com/sovr610/refffiy/tree/main/brain-ai-dev/skills/global-workspace-ignition
Command: npx skills add https://github.com/sovr610/refffiy --skill global-workspace-competition-broadcast-working-memory-with-ignition-dynamics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, pytest, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Orchestrates multi-modal encoder outputs into a fixed-capacity global workspace using ignition dynamics to gate broadcasts and memory updates, enabling stable, cross-modal integration across timesteps.

Core Features & Use Cases

  • Deterministic, fixed-modality token competition with explicit 4-term scoring (content, salience, novelty, task) and top-K gating.
  • Iterative rounds with ignition dynamics that decide when to commit broadcasts and persist workspace content in memory.
  • Slot-based workspace outputs of shape (B, K, D) with broadcast adapters to temporal, symbolic, and decision modules.
  • Extensible architecture supporting modular encoders, working memory backends, and telemetry-friendly logging.
  • Use cases include robust multi-modal reasoning, conscious-like broadcasting across cognitive modules, and persistent context across time.

Quick Start

Instantiate the workspace with sample modalities and run a single forward pass to observe ignition-gated broadcast.

Frequently Asked Questions about Global Workspace Competition + Broadcast + Working Memory with Ignition Dynamics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fuse multi-modal encoder outputs into a shared cognitive workspace?▼

Fuse multi-modal encoder outputs by tokenizing and routing them into a constrained global workspace, applying ignition-driven broadcasting to coordinate downstream modules. This enables stable cross-modal integration across vision, text, audio, and sensor data.

How does ignition dynamics control broadcasting in a global workspace architecture?▼

Ignition dynamics control broadcasting by gating when the global workspace commits outputs and persists content in working memory. This mechanism uses iterative rounds with deterministic top-K gating to decide when to trigger cross-module broadcasts.

How do I implement deterministic winner selection for multi-modal tokens in PyTorch?▼

Implement deterministic winner selection using explicit four-term scoring across content, salience, novelty, and task metrics. The architecture applies top-K gating to select winning tokens from a fixed-modality pool, producing slot-based workspace outputs of shape (B, K, D).

Can I use this global workspace module with custom vision and audio encoders?▼

Yes, the architecture supports modular encoders for vision, text, audio, and sensor data. It provides broadcast adapters to route slot-based workspace outputs to temporal, symbolic, and decision modules, allowing integration with custom encoder implementations.

What is the best way to maintain persistent context across timesteps in a cognitive pipeline?▼

Maintain persistent context by applying ignition dynamics that gate memory updates within the working memory backend. The workspace persists content across timesteps, ensuring robust multi-modal reasoning and stable context coordination for downstream modules.

What are the limitations of using fixed-capacity workspaces for multi-modal token competition?▼

Fixed-capacity workspaces constrain the number of tokens selected via deterministic top-K gating, meaning overflow tokens are discarded. This requires careful tuning of the four-term scoring weights to ensure critical salience and novelty signals are not lost during slot allocation.