AQuA
Two LLM research loops (factor discovery, model development) reuse evidence from their own past experiments inside sealed sandboxes. Scored by IC and held-out Sharpe.
Research system for bounded recursive self-improvement, not a reusable benchmark. Design point: the sandbox fixes data splits, labels and evaluator, and agents act only through constrained factor expressions or config diffs. No code link found.
- Released
- August 2026
- Kind
- AI research tasks
- Level
- Research frontier
- Scored
- Checked automatically
- Answers
- Agent task
In the authors’ words
We study recursive self-improvement at the level of quantitative-investment research: whether an autonomous system can use evidence from earlier experiments to improve the hypotheses and candidates proposed in later iterations. We present AQuA, which comprises two separate language-model-driven research systems: one for symbolic factor discovery and one for trainable model development. Each system records experimental results and uses them to guide subsequent proposals. Each operates in a fixed sandbox, which fixes the data splits, feature and label definitions, and evaluator while allowing the model to act only through constrained factor expressions or configuration diffs. The factor system, a manager-mediated multi-agent pipeline, discovers and combines factors into a signal that reaches a combined validation information coefficient of about 0.190 on a crypto universe. The model system, a config-driven loop over a hybrid time-series architecture, reaches a per-stock information coefficient of +0.0843 on US equities and converts it into a threshold long/short strategy with a held-out Sharpe of up to +2.50 at a two-leg cost. The strategy is positive in every year from 2021 to 2025.
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for AQuA yet.
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
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