Reflexive Intelligence: Decision-Making in Observer-Participant Environments

We introduce Reflexive Intelligence, a framework for AI decision-making 
in Observer-Participant Environments (OPEs) — systems where an agent's 
actions causally alter the environment it seeks to predict. Unlike 
conventional reinforcement learning benchmarks operating in 
observer-invariant settings, OPEs are characterized by reflexivity: 
participant beliefs and actions recursively reshape system dynamics. 
We formalize this distinction, identify the Reward Interaction Problem 
in multi-objective GRPO training, and present empirical findings from 
a financial market implementation using a 3B active-parameter MoE model. 
Results suggest reflexive reasoning capabilities can be induced through 
targeted training methodologies even in smaller models, with implications 
for AI deployment in financial markets, policy systems, and social platforms.