Siyuan Wang

This work extends SocioVerse 1.0 into a human-AI co-evolutionary paradigm built from two loops and one infrastructure, and validates SocioVerse2 across three case families and seven case studies, from reproducing canonical agent-based models to modeling policy processes on real records and nowcasting macro-economic indices beyond the response model's knowledge cutoff.

LLM research agents consistently lose to a naive moving-average baseline at forecasting scientific trends—not due to poor reasoning, but because asking models to predict the future systematically biases their search policies away from the most recent literature.

PRQ-KMeans achieves up to 11.8% improvement in recommendation performance by rethinking how semantic identifiers are tokenized.