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Hugging Face Compute Grant Application
MiniCrit-70B: Scaling Adversarial Validation
for AI Safety in Financial Markets

PATENT PENDING - USPTO #63/922,623
William Ousley (HuggingFace: wmaousley)
Antagon Labs
Compute Request: 500 GPU-hours (A100 80GB)

PROJECT SUMMARY

MiniCrit introduces adversarial multi-agent validation for algorithmic trading systems, using specialized LLM critics to challenge trading rationales before execution. Our proof-of-concept MiniCrit-1.5B model (published on HuggingFace) demonstrates feasibility with 12,132 adversarial training pairs. This grant request funds scaling to MiniCrit-70B for production deployment in live trading systems.

Key Innovation: First application of multi-agent adversarial LLMs to financial AI safety, now protected by provisional patent (USPTO #63/922,623, filed November 21, 2025).

WHY THIS MATTERS TO HUGGING FACE COMMUNITY

1. Massive Open-Source Dataset Contribution

We've already published 12,132 adversarial rationale-critique pairs under CC-BY-4.0 license on HuggingFace. With this grant, we'll expand to 50,000+ pairs covering:

• 20+ asset classes (equities, futures, crypto, options)
• 1,000+ flaw types (overfitting, regime mismatch, statistical errors)
• Multiple market regimes (bull, bear, crisis, recovery)
• Multi-LLM perspectives (6 different models)

This becomes the largest open-source adversarial critique dataset for financial reasoning -valuable for:

• Training other adversarial validation systems
• Research on multi-agent LLM safety
• Critique generation methodology studies
• Benchmarking reasoning quality across LLM families

2. Novel AI Safety Research

MiniCrit demonstrates how adversarial multi-agent architectures can provide safety oversight for autonomous systems. Research questions include:

• How does multi-agent critique compare to single-model validation?
• What meta-scoring functions best aggregate conflicting critiques?
• Can specialized critics outperform general-purpose models?
• How do we scale adversarial validation to production?

3. Production Deployment Case Study

MiniCrit-70B will be deployed in live trading systems, providing real-world data on:

• Performance under production constraints (latency, cost, reliability)
• False positive/negative rates in high-stakes environments
• Operational best practices for multi-agent LLM systems

EXISTING WORK ON HUGGING FACE

Published Models:

• MiniCrit-1.5B: https://huggingface.co/wmaousley/MiniCrit-1.5B

Published Datasets:

• 12,132 adversarial rationale-critique pairs (CC-BY-4.0)

Community Engagement:

• Active repository with documentation
• Model card with training details and evaluation metrics
• Example usage and inference code

Impact: Our work demonstrates a complete pipeline from adversarial data generation → model training → production deployment. This grant enables us to scale the entire pipeline to production-grade 70B parameters.

COMPUTE REQUIREMENTS

Requested Hardware:

8x A100 80GB GPUs (500 GPU-hours total)
Training Plan:

• Phase 1 (200 hours): Initial 70B training on expanded 50k dataset
• Phase 2 (200 hours): Fine-tuning with specialized critic configurations
• Phase 3 (100 hours): Meta-scoring engine optimization

Why A100 80GB:

70B parameter model requires large memory for efficient training. A100 80GB enables batch sizes that make adversarial training practical at scale.

Timeline:

Complete training in 3-4 months with immediate publishing of checkpoints and final model to HuggingFace.

DELIVERABLES TO COMMUNITY

1. MiniCrit-70B Model [OPEN SOURCE]
   Production-grade 70B adversarial validation model published on HuggingFace with:

• Complete model weights
• Detailed model card with training methodology
• Inference code and examples

2. Expanded Dataset [CC-BY-4.0]
   50,000+ adversarial rationale-critique pairs across:

• 20+ asset classes
• 1,000+ flaw categories
• Multiple market regimes
• Multi-LLM perspectives

3. Training Documentation

• Complete training scripts and configuration
• Adversarial data generation pipeline
• Best practices for multi-agent training

4. Research Publications

• Technical report on adversarial validation architecture
• Blog posts on HuggingFace
• Conference paper submissions (NeurIPS, ICML)

5. Production Deployment Insights

• Real-world performance metrics from live trading
• Operational best practices document
• Latency/cost/reliability analysis

INTELLECTUAL PROPERTY STRATEGY
Patent Protection: Core algorithms and orchestration methods protected via provisional patent (USPTO #63/922,623, filed November 21, 2025). Non-provisional application planned within 12 months.
Open Source Contribution: Model weights, training data, and methodology documentation published on HuggingFace under permissive licenses (CC-BY-4.0 for data, Apache 2.0 for code).
Rationale: Strategic IP approach maximizes research impact while enabling commercialization. Community benefits from open models and datasets. Patent protection covers production deployment methods and specific architectural innovations.
WHY FUND THIS PROJECT?
PROVEN TRACK RECORD: We've already delivered MiniCrit-1.5B and 12k+ training pairs - exceeding targets by 242%. Infrastructure validated and ready to scale.
MASSIVE DATASET CONTRIBUTION: 50k+ adversarial critique pairs will be largest open-source financial reasoning dataset. Valuable for entire AI safety research community.
NOVEL RESEARCH: First application of multi-agent adversarial LLMs to production financial systems. Demonstrates AI safety in high-stakes environments.
COMMUNITY ENGAGEMENT: Active HuggingFace presence with comprehensive documentation, model cards, and usage examples. Committed to open-source contribution.
BROADER IMPACT: Adversarial validation framework extends beyond finance to medical AI, autonomous vehicles, critical infrastructure. Research benefits entire autonomous systems field.
CONTACT
William Ousley
HuggingFace: wmaousley
Antagon Inc. (DBA Antagon Labs)
Existing work: https://huggingface.co/wmaousley/MiniCrit-1.5B