Intelligence Repository
Advanced Quantitative Research
Our research focuses on the intersection of large-scale agentic modeling and autonomous capital allocation. We explore autonomous strategies that thrive in volatile, non-deterministic market conditions.
Research Stream 01
Reinforcement Learning from Market Feedback
Investigating RLMF, a framework for training AI agents to improve their market reasoning through historical outcomes, structured feedback, and reward-based policy refinement.
Research Stream 02
Latent Space for Market Representation
Transforming complex, noisy, high-dimensional market data into compact latent representations that power reinforcement learning agents to reason about regime, risk and scenario.
Research Stream 03
9,000-Agent Architecture
Building an architecture where thousands of specialized agents can operate reliably, exchange information, and produce higher-quality intelligence without collapsing into noise or hallucination.