Agenthon 2026: Verifiable AI for Quantitative Finance (Executable Oracles for Code, Forecasts, Simulation, and Market Analysis)

Agenthon 2026 asks one question: can modern AI systems produce finance-relevant outputs that survive executable, leakage-controlled verification? The competition runs four parallel tracks: (1) coding agents for quantitative finance, (2) probabilistic multivariate financial forecasting, (3) semantic-preserving market simulation, and (4) retrieval-grounded financial analysis. Each track ships public development tasks, sealed held-out tasks built the same way, fixed resource and leakage constraints, executable admissibility checks, and ranking metrics published before launch. Track 1 uses exact executable correctness oracles; Tracks 2–4 use executable scoring protocols (statistical, simulator-constrained, and auditable-agent respectively). Official scoring runs in a closed-resource environment with a fixed compute budget and no arbitrary outbound Internet; each track ships an open-weights baseline so low-budget teams can compete without commercial APIs. We release a reusable evaluation protocol together with a reproducible failure-mode analysis. The Alphathon program has operated as an annual research-training competition since 2024, organized by the Society of Quantitative Analysts (SQA, founded 1965) and CEWIT at Stony Brook University; the 2024 and 2025 editions drew 323 cumulative applicants from 37 U.S. states and 12 countries. The 2026 NeurIPS edition expands that program into Agenthon 2026, an open, automated, oracle-based competition for executable and agentic AI systems in quantitative finance.