MiroFish GitHub Project Breakdown: Multi-Agent AI vs Real Markets - Polymarket, Kalshi & Predictions

MiroFish: https://github.com/666ghj/MiroFish MiroFish is a multi agent AI prediction engine that simulates thousands of personas to model real world outcomes. This video breaks down how the MiroFish GitHub project works, including its GraphRAG architecture, swarm intelligence system, and high cost simulation pipeline. You’ll see why MiroFish struggles in trading environments due to latency, RLHF bias, and consensus collapse, and how a deterministic AI trading model compares. We analyze MiroFish against a structured adversarial framework focused on speed, confidence calibration, and execution timing. If you’re building an AI trading system, this comparison highlights where MiroFish fits and where it fails. Timestamps: 0:00 MiroFish AI system overview 0:37 MiroFish swarm vs deterministic trading model 1:14 Cost latency and accuracy comparison 1:43 MiroFish GraphRAG architecture explained 2:36 Token usage and infrastructure limits 3:20 RLHF bias and agent consensus collapse 4:32 Agent collapse and failed adversarial behavior 5:00 Deterministic AI trading framework breakdown 5:42 Confidence delta execution model 6:14 Reflect agent feedback loop and performance MiroFish demonstrates how multi agent AI can simulate complex systems, but trading demands speed, precision, and disagreement. Deterministic AI trading models with adversarial structure, confidence thresholds, and reflect agent feedback outperform swarm intelligence where latency and consensus bias erase edge in fast moving financial markets. #MiroFish #AITrading #SwarmIntelligence