Research – DrNamLabs

Research Overview

My research focuses on understanding and engineering adaptive intelligence in complex systems — from artificial life and evolutionary computation to large-scale collective behavior and emerging LLM-based cognitive architectures. I study how agents learn, coordinate, and evolve in dynamic environments, and how these principles can translate into real-world intelligent systems, including quantitative modeling and decision-making frameworks.

Research Themes

1. Evolutionary Computation & Artificial Life

I develop algorithms and systems where adaptive behavior emerges from evolutionary and decentralized processes. My work explores:

Neuroevolution and high-dimensional evolutionary search
Emergent self-organization and collective reproduction
Swarm intelligence under ecological constraints
Dynamical systems approaches to adaptive morphology

2. Collective Intelligence & Multi-Agent Systems

I study how coordination, agency, and structure arise from interacting populations of agents:

Distributed behavior and emergent strategies
Agent-based modeling in complex adaptive landscapes
Information flow and self-organizing collective dynamics

3. LLM-Based Cognitive Systems & Hybrid Agents

I design hybrid architectures where large language models interface with evolutionary, cellular, or swarm-based systems. This line of work investigates:

LLM-driven control of agent collectives
Natural language-guided adaptation
Cognitive-scientific modeling of reasoning and behavior
Bridging symbolic reasoning with dynamical intelligence

4. Quantitative Modeling & Adaptive Market Intelligence

Translating principles from complex systems into applied quantitative research. This includes:

Fractal and multi-scale market structure analysis
Regime detection and state transition modeling
Machine learning–driven signal generation
Risk engines based on adaptive dynamics
LLM-enhanced sentiment and volatility inference

This line of research forms the foundation for Bunny Quant.