arXiv AI By Yuyang Dai, Xueqing Peng, Lingfei Qian, Zhuohan Xie

Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation

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arXiv:2606. 17459v1 Announce Type: new Abstract: Evaluating the decision-making capabilities of large language models (LLMs) is a growing research priority, yet existing benchmarks focus on isolated cognitive tasks such as reasoning, knowledge retrieval, and economic rationality in stylized settings.

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arXiv AI
Aug 28

DSA: Evidence-Aware LLM-Agent Orchestration for Multi-Market Stock Research

The paper introduces DSA, an evidence‑aware orchestration framework that uses large language model agents to conduct multi‑market stock research. DSA structures the workflow into stages of evidence acquisition, context construction, model‑routed analysis, optional role and Strategy Skill reasoning, and report generation, offering both a default and an agentic profile with distinct output validation and risk safeguards. The reference implementation supports six regional markets, fifteen Strategy Skills, and multiple execution surfaces, and has passed 1,457 portable offline backend contract tests, confirming implementation conformance.

By Linsen Zhu, Yi Shi