Matrix — Neural Task Intelligence

Describe what you want to do. Matrix finds the right skill, tool, or agent.

Matrix is a trained neural retrieval model that searches 100,000+ AI capabilities — skills, tools, agents, and apps — using semantic understanding rather than keyword matching.

Try It

Web: matrix.hyper.space

CLI:

hyperspace search matrix "deploy my app to kubernetes with monitoring"

API:

curl -X POST https://matrix.hyper.space/api/search \
  -d '{"query": "optimize SQL queries", "top_k": 10}'

Architecture

┌──────────────┐   ┌──────────────┐   ┌──────────────┐   ┌──────────────┐   ┌──────────────┐
│   MATRIX     │   │   Users      │   │   Feedback   │   │   Retrain    │   │   Better     │
│   deployed   │──▶│   search     │──▶│   👍 👎       │──▶│   on real    │──▶│   results    │
│   everywhere │   │   tasks      │   │   outcomes   │   │   usage      │   │              │
└──────────────┘   └──────────────┘   └──────────────┘   └──────────────┘   └──────┬───────┘
                                                                                   │
                   compounds: more users → more signal → smarter model             │
                   ╰──────────────────────────────────────────────────────▶ ♻       │

Model

• Backbone: Qwen2.5-1.5B (frozen)
• Retrieval head: ColBERT 128-dim with distillation from text-embedding-3-small
• Training: 189K samples (139K synthetic + 50K hard-negative triplets), 30 epochs on GPU
• Capabilities indexed: 100,000+ (21K tools + 43K skills + 1.2K agents + 50 mobile apps)
• Domains: code, web, data, infra, docs, agents, mobile, blockchain

Search Pipeline (Hybrid RRF)

Production search uses Reciprocal Rank Fusion combining:

1. Vector search — OpenAI text-embedding-3-small (1536-dim, pgvector)
2. Full-text search — PostgreSQL tsvector + BM25 + pg_trgm
3. Neural routing — Matrix-2 ColBERT domain classification

Results are fused using RRF (k=60), same approach as qmd.

P2P Integration

Matrix is the 9th primitive in the Hyperspace Gossiping Agents Protocol:

/hyperspace/matrix/1.0.0

Gossip topics:
  hyperspace/matrix/feedback    — user votes from all nodes
  hyperspace/matrix/discovery   — new skills gossipped
  hyperspace/matrix/model       — weight updates after retraining

Every node can run Matrix locally (~4GB VRAM). Feedback from all nodes aggregates into training data via gossip. The model improves from the network, for the network.

Training

# Generate distillation data (mines hard negatives from OpenAI embeddings)
python3 train_v5_fast.py --epochs 30 --batch-size 1024 --lr 2e-4

# v5 results: loss 1.93 → 0.155 in 93 min on GPU
#   distill_loss: 0.86 → 0.30 (ColBERT aligned 70% with OpenAI)
#   contrastive_loss: 0.05 → 0.01 (strong discrimination)

Experiment Log

Version	Date	Capabilities	Retrieval Quality	Notes
v2	2026-02-11	31,900	53.9% ret@1	First working retrieval
v3	2026-02-28	100,000+	5.9% ret@1	2x capability space, quality dropped
v4	2026-03-08	100,000+	19.1% ret@1	30 epochs, but _orig_mod bug in deploy
v5	2026-03-19	100,000+	TBD	Distilled from OpenAI, 93 min GPU

License

Part of the Hyperspace open network.