Two benchmarks posted to arXiv in the opening days of October 2026 turn the two things every other agent eval holds constant into variables — how much guidance the harness supplied, and whether the score rewards a prediction or an explanation. Both move the number by tens of points, and one of them reports an agent at 47.4% predictive accuracy against a human scientist’s 48.8% while scoring 29.4% against 69.7% on the insight the task was built around.
AI in the Trading Stack: What Hedge Funds Actually Run on the Decision
AI in trading is not one bot; it is a four-layer stack — signal, sizing, execution, risk — and each layer runs a different model with different failure modes. Map the layers and any "AI hedge fund" headline becomes legible in thirty seconds.
Agentic AI for Trading Research: When the LLM Sits in the Loop
The hype says AI agents run the fund; the reality in 2026 is that LLM agents run the research desk — fundamentals, sentiment, bull-bear debate, risk sign-off — while rule-based code still pulls the trigger. Knowing where the line sits is the difference between deploying the pattern and over-trusting it.