A bad experiment is bad enough. A self-improving agent can keep building on it.

New Stanford, Princeton, Ant group paper.

AQuA targets that failure mode: if an agent writes a leaky experiment, gets a great score, and stores it as precedent, recursion can amplify the bug.

The authors saw this in an earlier version, where an LLM-written volume feature quietly used end-of-day information and a second LLM reviewer still approved it.

Their fix is architectural.

Agents can propose factors or model changes only through restricted specifications; the data path, labels, splits, and evaluator stay sealed outside their reach.

Search sees validation scores, while the final test window stays untouched until the configuration is frozen.

On US equities, the hybrid model reaches +0.0843 IC versus +0.0613 for the strongest baseline, and its long/short strategy reaches +2.50 Sharpe at 2 bps, with about +2.0 under fully causal walk-forward testing.

These are simulated, not live-trading results.

For autonomous research agents, the design rule is useful: let the agent improve the experiment without letting it redefine valid evidence.

• arxiv. org/abs/2608.12841

Title: "AQuA: Recursively Self-Improving Quantitative Trading Research Agents"