Quantitative research agents that write their own experiments can corrupt the evidence they later learn from. A leaky feature that scores well gets stored as a successful precedent and propagated through later iterations. Prompt-level instructions and reviewer agents do not close this, because author and reviewer share the same blind spots. A team of researchers from Princeton University, Ant Group and Stanford University propose AQuA. AQuA is a pair of language-model-driven research systems that improve their own research process across iterations while the thing judging them stays frozen. One discovers symbolic alpha factors on crypto; the other develops time-series models on US equities. They share no agents, memories, candidate spaces or research state.
The failure mode AQuA is built around
Quantitative research breaks on small methodological errors that produce convincing but non-reproducible backtests, documented since Bailey et al.. An agent writing its own experiments makes this worse: a leaky feature that scores well gets stored as precedent, and recursion amplifies an undetected bug as readily as a real discovery.
Prompt-level instructions and model review are not an integrity boundary. Repeated access to a fixed holdout causes adaptive overfitting, and LLM agents have been observed exploiting misspecified objectives and evaluators. AQuA instead makes leakage-inducing actions unavailable. Each part fixes its splits, feature and label definitions and evaluator before any iteration starts, and the agent emits only a constrained factor expression or a single config diff. The research team call this asymmetric freedom: the agent explores freely inside its DSL, but the evaluator sits outside the adaptive surface. What improves is the research process.
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