Pre-Alpha Stage
DeML OS
The infrastructure to execute, verify, settle, and constrain ML autonomously.
Research Scope
Defining the boundaries of decentralized machine learning systems
In Scope
- ML execution under non-centralized scheduling
- Trust and correctness of ML in untrusted environments
- Native pricing and settlement mechanisms that support long-term operation
Out of Scope
- ✗ Systems that only change payment methods without changing ML execution
- ✗ Pure CPU / GPU compute marketplaces
- ✗ AI products or agent applications
System Dimensions
Key evaluation criteria for decentralized ML systems
Execution
Inference, training, and task scheduling mechanisms
Verification
Trust models, correctness proofs, and dispute resolution
Incentive
Pricing models, incentive mechanisms, and sustainability
Governance
Upgradeability, decision-making, and tokenomics
Recent Research Notes
Latest developments in decentralized ML
Paper
Trust-Aware Routing for Distributed Generative AI Inference at the Edge
Chanh Nguyen Edge
Paper
Real-Time AI Service Economy: A Framework for Agentic Computing Across the Continuum
Lauri Lovén Edge Computing
Paper
MoEless: Efficient MoE LLM Serving via Serverless Computing
Hanfei Yu Serverless
Paper
MoE-Sieve: Routing-Guided LoRA for Efficient MoE Fine-Tuning
Andrea Manzoni MoE
Paper
Route Experts by Sequence, not by Token
Tiansheng Wen Routing
Join the Research Community
If you are interested in distributed systems, ML systems, verifiable computation, or crypto-economic mechanisms at the system layer, you are welcome to join the discussion.