Zilli v3.0 - MOM Meta-Intelligence System

Overview

Zilli v3.0 is a self-evolving engineering platform purpose-built for AI to autonomously design, develop, test, optimize, and deploy MOM (Model of Models) meta-intelligence systems. Its core philosophy fuses swarm intelligence orchestration (MOM technology) with dual-model collaborative engineering (Zilli's original architecture), delivering high-performance, low-cost, data-sovereign private MOM deployments for data-sensitive enterprises.

In Zilli v3.0, MOM is not just the final product — its key techniques (task DAG decomposition, meta-evaluation models, multi-objective optimization) are applied back into Zilli's own development pipeline, creating a "building MOM with MOM" closed loop.

Zilli is maintained by Ethercoin, running on the Ethercoin decentralized compute network (12,847+ nodes / 356 PFLOPS) with ZK+TEE+PoRW verification for trusted training and inference.

Dual-Layer Architecture

Zilli v3.0 employs a dual-layer architecture: the top meta-orchestration layer (MOM kernel) handles swarm intelligence coordination; the bottom development pipeline layer (Zilli engine) handles automated tool production.

Top Layer: MOM Meta-Orchestration (Runtime)

An operating system above models: decomposes complex requests into parallelizable task DAGs, dynamically selects optimal model ensembles based on capability profiles, predicts performance via meta-evaluation models, and achieves Pareto optimality across quality, cost, latency, privacy, and throughput.

Bottom Layer: Zilli Pipeline (Build-time)

Dual-model collaboration (SOTA Planner + cost-effective Executor) executes the Plan → Generate → Verify → Reflect → Evolve loop. The Planner handles deep reasoning and reflection (<5% of calls), while the Executor handles 95% of generation work, continuously evolving through distillation and RL.

Five-Phase Execution Pipeline

Plan — SOTA-driven

Decomposes high-level requirements into task DAGs with I/O schemas, acceptance criteria, and permitted tools. Generates orchestration files marking key nodes requiring Planner assistance.

Generate — Executor-driven

Executor runs leaf tasks in parallel; Planner correction mode activates when confidence drops below threshold, performing single-pass Critic-Edit. Produces code, configs, tests, and documentation.

Verify — Layered automation

Static analysis → sandbox testing → behavioral consistency checks → Planner review (high-risk tasks). Auto-retry on failure (up to 3 times), injecting error context each round.

Reflect — Deep-dive by Planner

Analyzes the full execution trajectory, generates root cause classification (planning quality / generation quality / environment / requirement drift), and extracts success patterns into the trajectory memory store.

Evolve — Closed-loop learning

Three parallel paths: instant strategy updates, training data accumulation, and periodic distillation with RL training. The system improves with every cycle.

MOM Meta-Orchestration Core

The MOM kernel is the intelligence center of Zilli v3.0, responsible for optimal multi-model swarm intelligence scheduling at runtime.

Task Decomposer

Recursively breaks complex requests into DAG sub-tasks, analyzes dependencies and parallelism, and automatically identifies compute-bound vs I/O-bound nodes.

Model Capability Profiler

Maintains multi-dimensional radar charts per model (reasoning, coding, math, creativity, instruction following, safety, etc.) with ELO ratings and decay-based updates.

Meta-Evaluation Model

Predicts each model's performance on specific sub-tasks proactively, balancing exploration and exploitation via Bayesian optimization and Thompson Sampling.

Multi-Objective Optimization Engine

Solves for Pareto-optimal model-to-task bindings across five dimensions: quality, cost, latency, privacy, and throughput. Supports weight-based preferences and constraint solving.

Dynamic Cost Control

Three-tier budget management: monthly budget + hourly quota + emergency mode. SOTA model calls are kept below 5% of total calls and 10% of total cost.

Enterprise Privacy Governance

Zilli's privacy module provides end-to-end data governance for sensitive enterprise workloads. Five data classification levels drive automated policy enforcement across the entire Agent lifecycle.

Five-Level Data Classification

PUBLIC / INTERNAL / CONFIDENTIAL / RESTRICTED / REGULATED. Automatic PII/PHI detection elevates data containing personal identifiers to at least CONFIDENTIAL.

PrivacyGatekeeper

Makes local/cloud/deny decisions based on data classification and tenant policy. RESTRICTED and REGULATED data is forced to local execution.

Compliance Reports

Generates GDPR, HIPAA, SOC2 compliance reports from existing audit trails (JSONL format) out of the box. No additional storage infrastructure required.

Training & Distillation

The Executor model evolves continuously through SFT + RL (CISPO/GRPO) + distillation, achieving "cost-effective AI writes AI".

Training Data Pipeline

Automatically collects successful trajectories as positive samples; failed tasks are corrected by Planner reflection; human feedback boosts sample weights.

Layered Experience Replay

Golden trajectories (reward > 0.8) are stored directly; low-reward trajectories are corrected by Planner and stored in the failure reflection pool. Mixed sampling for training.

Executor-only Evaluation

Planner is completely disabled to verify the Executor's standalone capability. Pass criteria: core task success rate ≥85%, cost < 5% of SOTA.

Tech Stack

Languages

Python 3.11+, Rust (zilli-rs kernel)

Workflow Engine

Temporal / Prefect

RL Algorithms

CISPO, GRPO, OpenRLHF

Vector + Graph DB

Milvus / Qdrant + Neo4j / FalkorDB

Sandbox

K8s + Firecracker microVM

Repository

github.com/ethercoinai/Zilli →