While multi-agent systems based on large language models (LLMs) have shown promise in automating academic research workflows, extending them to economics and business research presents two key challenges, as specialized domain knowledge often spans neighboring disciplines yet remains difficult to access in a structured manner. First, existing methods primarily retrieve information at the paper level, whereas the evidence required for research tasks is often distributed across different sections, creating a granularity mismatch that limits retrieval coverage and precision. Second, these fields demand strict empirical rigor, yet current systems provide limited mechanisms for learning from evaluation feedback. We present BizSage, a multi-agent framework that combines corpus-level fine-grained retrieval with quality-driven self-evolution. We construct a Lateral Knowledge Graph (LKG) by integrating section-level knowledge graphs and apply Personalized PageRank (PPR) to identify sections that are both semantically relevant and structurally important. Seven specialized agents collaborate under a Meta-Review self-evolution mechanism that distills failure modes from evaluation traces into reusable strategies. On a benchmark spanning four domains and three tasks, BizSage ranks first on the majority of metrics, achieves pairwise win rates above 60% against six baselines, and produces zero hallucinated citations. We hope BizSage paves the way for reliable research assistance in economics, business, and the broader social sciences.

LLM agents are widely used in social simulation, yet fluent social reasoning does not guarantee valid decision mechanisms. We test whether models implement socially embedded utility, where actions depend on peer-weighted references rather than isolated private preference. We introduce a theory-guided benchmark from network utility theory covering utility recovery, social-comparison gating, and structural transfer across two out-of-distribution decision forms. Across open-source and proprietary models, we find a gap between cue recognition and peer aggregation, spurious social activation when comparison should be inactive, and declining accuracy as social dependence rises. We propose theory-supervised mechanism alignment trained on latent utility states. Fine-tuning yields +8.3 and +13.9 utility-recovery points on Llama-3.1-8B and Qwen3-8B, raises gating above 95%, and improves transfer to unseen structures. Multi-agent simulations show that repairing local utility shifts macro outcomes, including stronger financial amplification and more stable labor stratification, which indicates that realistic macro-level simulations require adjustments at the individual level. Data and code are available at https://github.com/saaranLiu/SEDM-alignment.