🧠 DATAFORGE: A Synthetic Data Subnet on Bittensor

Overview

Synapse is a proposed subnet in the Bittensor ecosystem dedicated exclusively to high-quality, domain-specific synthetic data generation.

While compute and inference subnets already exist within Bittensor, there is currently no subnet whose primary output is structured, benchmark-validated synthetic training data arguably the most scarce and valuable resource in modern AI systems.

Synapse fills this gap.

Our thesis is simple:

The future bottleneck of AI is not compute it is high-quality data.

Synthetic data that measurably improves downstream model performance represents one of the purest and most objective forms of Proof of Intelligence.

1. Subnet Design Proposal

1.1 Incentive & Mechanism Design

Core Mechanism

Miners generate synthetic datasets.
Validators objectively score them via downstream model performance.

The fundamental loop:

1. Task issued (e.g., domain-specific dataset generation)

2. Miner submits structured synthetic dataset

3. Validator trains a small reference model on submitted data

4. Model is evaluated on a hidden benchmark set

5. Score = measurable performance improvement

6. Emissions distributed proportionally to contribution quality

This makes model improvement the proof itself.

1.2 Emission & Reward Logic

Emission Allocation

• 70% → Miners (performance-weighted)

• 25% → Validators (scoring accuracy & participation consistency)

• 5% → Protocol treasury (future research, benchmark curation)

Miner Reward Formula

Let:

• B = baseline benchmark performance

• M = model performance after training on miner dataset

• Δ = M - B

Reward ∝ normalized Δ across all miners in epoch.

Miners are paid strictly for measurable intelligence contribution.

Validator Reward Formula

Validators are rewarded based on:

• Scoring consistency with peer validators

• Proper evaluation execution

• Timely participation

Validators with statistically deviant scoring are slashed.

1.3 Incentive Alignment

For Miners

• Incentivized to generate:

  • High-quality

  • Diverse

  • Domain-accurate

  • Non-duplicative

• Low-quality data yields zero benchmark lift → zero reward

For Validators

• Incentivized to:

  • Accurately measure performance

  • Avoid collusion

  • Maintain benchmark integrity

• Misaligned scoring leads to slashable stake

1.4 Anti-Adversarial Mechanisms

• Hidden evaluation sets (rotated periodically)

• Duplicate detection across submissions

• Randomized validator cross-evaluation

• Stake-weighted consensus scoring

• Slashing for outlier or malicious validators

Garbage data cannot pass because:

• If it doesn’t improve the model → it doesn’t get paid.

• If it copies public datasets → benchmark overfitting detection flags it.

1.5 Proof of Intelligence

Synthetic data generation that improves downstream models requires:

• Semantic reasoning

• Domain expertise

• Distribution modeling

• Error correction

• Diversity control

Since rewards are tied to measurable performance lift, this subnet constitutes a genuine:

Proof of Intelligence via Model Improvement

This is one of the cleanest PoI implementations possible in Bittensor.

1.6 High-Level Algorithm

Task Assignment

• Validator posts structured generation task:

  • Domain

  • Schema

  • Size requirement

  • Constraints

Miner Submission

Input:

{
  "task_id": "...",
  "domain": "legal_summarization",
  "schema": {...},
  "num_samples": 500
}

Output:

{
  "dataset": [
    {"input": "...", "output": "..."},
    ...
  ]
}

Validation Flow

1. Train reference model on dataset

2. Evaluate on hidden benchmark set

3. Compute Δ performance

4. Normalize across miners

5. Distribute rewards

2. Miner Design

Miner Tasks

• Instruction-following dataset generation

• Domain-specific Q&A creation

• Multi-turn dialogue synthesis

• Edge-case generation

• Structured reasoning examples

Input → Output Format

Input:
Task specification JSON

Output:
Structured dataset in defined schema

Performance Dimensions

• Quality (benchmark lift)

• Diversity

• Schema compliance

• Dataset size adherence

• Submission latency (secondary)

Primary metric = performance improvement.

3. Validator Design

Scoring Methodology

Validators:

1. Train standardized lightweight reference model

2. Use fixed hyperparameters

3. Evaluate on secret test set

4. Publish score

Final score = median of validator scores.

Evaluation Cadence

• Epoch-based (e.g., every 6–12 hours)

• Benchmark sets rotated weekly

• Domains rotated per cycle

Validator Incentive Alignment

• Stake required

• Outlier detection

• Slashing for dishonest scoring

• Rewards tied to scoring accuracy

4. Business Logic & Market Rationale

The Problem

AI systems are running out of high-quality, domain-specific training data.

Web-scraped data:

• Is saturated

• Is legally constrained

• Lacks domain precision

Synthetic data is the solution — but centralized providers dominate.

Market Opportunity

• Synthetic data market ≈ $1.5B

• ~30% CAGR

• Growing demand from:

  • AI labs

  • Enterprise ML teams

  • Research institutions

Demand is structural and accelerating.

Competing Solutions

Within Bittensor:

• Compute subnets

• Inference subnets

• Model-serving subnets

None focus on data as the primary output.

Outside Bittensor:

• Centralized synthetic data startups

• Enterprise internal data teams

Synapse differentiates via:

• Decentralized intelligence

• Market-based quality filtering

• Objective performance benchmarking

Why This Fits Bittensor

Bittensor rewards measurable intelligence contributions.

There is no cleaner measurable signal than:

“Did this data improve model performance?”

This subnet transforms data generation into a verifiable intelligence market.

Long-Term Sustainability

Revenue streams:

• Dataset licensing to AI labs

• API access to top-ranked synthetic data pools

• Enterprise custom task bounties

• Research partnerships

Emission incentives bootstrap supply.
Enterprise demand sustains it long-term.

5. Go-To-Market Strategy

Initial Target Users

• Open-source AI teams

• Fine-tuning communities

• Domain AI startups (legal, medical, finance)

• Academic labs

Anchor Use Cases

• Legal summarization datasets

• Financial compliance QA datasets

• Healthcare triage dialogue data

• Enterprise customer support simulation

Distribution Channels

• AI research communities

• Open-source LLM ecosystem

• ML engineering forums

• Strategic partnerships

Incentives for Early Participation

For Miners

• Early emission bonuses

• Founder multiplier epochs

For Validators

• Increased early validator rewards

• Benchmark governance participation

For Users

• Free initial dataset access

• Early partner pricing

Currntly on the ideation Phase before proceeding to the building phase

looking to start fundraising with bittensor