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metadata

title: CRDT-Merge Convergence Lab
emoji: 🔬
colorFrom: gray
colorTo: blue
sdk: gradio
sdk_version: 5.29.0
python_version: '3.12'
app_file: app.py
pinned: true
license: other
license_name: BUSL-1.1
license_link: https://github.com/mgillr/crdt-merge/blob/main/LICENSE
tags:
  - crdt
  - model-merging
  - distributed-systems
  - convergence
  - neural-network
  - federated-learning
short_description: 'CRDT convergence proof: 100 nodes, 26 strategies'

CRDT-Merge Multi-Node Convergence Laboratory

Empirical proof that the two-layer CRDTMergeState architecture guarantees identical merged models across distributed nodes — regardless of merge ordering, network partitions, or strategy choice.

Patent — UK Application No. 2607132.4, GB2608127.3 | E4 Trust-Delta Architecture
Paper: Conflict-Free Replicated Data Types for Neural Network Model Merging
Library: crdt-merge v0.9.5

Experiments

1. Multi-Node Convergence

Simulates N distributed nodes (up to 100), each contributing a unique model tensor. Nodes merge in multiple random orderings. Verifies that all orderings produce bitwise-identical Merkle roots and resolved tensors.

2. Network Partition & Healing

Splits nodes into isolated partitions. Each partition gossips internally and converges to its own state. Partitions are then healed (full gossip resumes). Verifies that all nodes converge to the same state post-healing — the core CRDT guarantee.

3. Cross-Strategy Sweep

Tests every merge strategy (weight averaging, SLERP, TIES, DARE, Fisher, evolutionary, and 7+ more) for convergence on the same node set. Verifies that the two-layer architecture provides universal CRDT compliance regardless of strategy.

4. Scalability Benchmark

Measures gossip and resolve overhead from 2 to 100 nodes. Confirms that the CRDT merge operation (set union on metadata) remains sub-millisecond regardless of model size, while resolve time scales linearly with contributions.

Key Results

Metric	Result
Max nodes tested	100
Strategies verified	13/13 (no-base)
Convergence rate	100% across all orderings
Partition healing	Always converges
CRDT merge overhead	< 0.5ms
Bitwise reproducibility	Guaranteed
E4 trust convergence	0.000 max divergence, 3.84ms
E4 proof-carrying ops	167K build/s, 101K verify/s

How It Works

The two-layer architecture separates concerns:

Layer 1 (CRDTMergeState): Manages a set of model contributions using OR-Set CRDT semantics. Merge = set union — trivially commutative, associative, idempotent.
Layer 2 (Strategy): Applies any merge strategy as a deterministic pure function over the canonically-ordered contribution set. Same inputs → same outputs.

Since Layer 1 guarantees all replicas converge to the same set of inputs, and Layer 2 is deterministic, all replicas compute identical merged models.