AutoScientist-Quant: Self-Evolving Coding Agents for Automatic Research in Quantitative Investment

arXiv cs.AISeptember 1, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2608.28632v2 Announce Type: new Abstract: Large language model agents can discover alphas, yet current methods have three weaknesses. The search cannot adapt during the run, automation usually ends at alpha generation while library selection and model choice stay manual, and alpha discovery can read the test window through loop feedback or code problems. We present AutoScientist-Quant, a self evolving search process that regards quantitative research as one budgeted search problem. A single controller conditions every decision on the remaining budget, choosing at each round whether to improve, combine, pivot, or stop, which node to expand, how many alphas to generate, and how to retrie

This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.

• Forskning
• Agenter