Auditing LLM Trading Bridging Theory and Market Reality with the GT table in R

Image for article Auditing LLM Trading Bridging Theory and Market Reality with the GT table in R
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News Summary

  • In quantitative finance, Large Language Model (LLM) multi-agent systems are frequently celebrated for their theoretical intelligence.
  • Traditional backtests systematically ignore execution semantics and market microstructure realities.
  • In AI-driven trading systems, the primary risk is no longer the raw quality of the agent’s alpha signal; it is the cognitive latency required to generate that signal.
  • Yao & Zheng (2026) forces us to stop judging agent architectures by their abstract zekası, and start auditing them by the brutal financial reality of their execution timing.
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