🔄 Simulation Engine
The Simulation Engine is the core execution layer of NeuraLink. It transforms model training into an open, verifiable, and collaborative process—where every step is traceable, and every contribution is rewarded.
7.1 Key Components
Task Dispatcher
Routes training requests and data to available compute nodes
Model Executor
Performs local model training and returns intermediate weights and metrics
Parameter Tracker
Records training progress, accuracy metrics, and node signatures on-chain
Verifier Engine
Validates results via consensus among multiple verification nodes
Reward Allocator
Distributes token incentives based on performance and successful validation
7.2 Task Lifecycle
Task Creation: Users or organizations define model structure, objectives, budget, and criteria
Data Selection: Tasks may use public datasets or request new data submissions
Training Participation: Nodes opt into tasks, perform training locally, and submit results with proof
Result Verification: Validators confirm accuracy, performance, and reproducibility
Reward Distribution: Trainers, validators, and data providers are compensated in Neura tokens
Model Completion or Continuation: Models either finalize (upon reaching targets) or loop into further refinement
7.3 Model Versioning & Evolution
Every model on NeuraLink is represented by an on-chain Model Object, which includes:
Unique ModelID and version number (e.g., M-23-v4)
Full traceability of all training iterations
Associated datasets, participant nodes, and metric logs
Support for forked models and custom training branches
DAO-based approval for publishing models in the marketplace
Community-driven delisting or modification of unethical models
This structure creates a Decentralized Model Repository, where models are reproducible, auditable, and co-evolved by global contributors.
7.4 Supported Model Types & Frameworks
Text Models
BERT, GPT, T5 fine-tuning and classification
Image Models
CNNs, Vision Transformers, Diffusion Models
Multimodal
Image-text alignment, audio-text pairs, VQA models
Generative AI
Text/image generation, autonomous planning, simulation
AI Agents
Multi-round dialogue, task-oriented reasoning, tool integration
Custom Models
Supports ONNX, PyTorch, TensorFlow submissions with standardized interfacing
NeuraLink enables simulation to become a community-driven process—turning intelligence from an outcome into an ecosystem.
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