Propose, Don't Judge: An Anytime-Valid Referee for LLM Agents That Mine Investment Factors
On the map: LLM agents and tool use → Financial trading and forecasting agents
Abstract (arXiv)
Language-model agents now run the whole of quantitative factor research: they propose investment factors, backtest them, select the survivors and retire them. We ask which of those jobs an agent should keep. Our answer is governed self-evolution: the agent may propose, and a frozen statistical referee that the agent cannot touch must judge. The referee scores each candidate only on market outcomes revealed after submission, by betting, so its false-discovery guarantee holds at every stopping time for any proposal policy. We cross three proposers (a script, a bandit and a language model) with this referee and with three deliberately leaky ones, in a synthetic world with planted truth, a probe-authoring environment and a ten-year walk-forward on the CSI 500. Who judges sets the number of false admissions: the frozen referee admits 5-11 times fewer sub-threshold factors than the leaky referees under a scripted proposer, and no proposer closes that gap. Who proposes sets the yield: the language model beats the script, matches the bandit, and adds the one capability a bandit lacks, writing its own diagnostic probes. The certificate's price is time: an admitted true factor waits about 500 trading days, and the certified portfolio's Sharpe ratio therefore trails an ungated one. Judging belongs to the procedure; proposing and instrument-making belong to the agent.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 16 of its 27 references that are in the map. First posted on arXiv 22 Sep 2026.
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16 of 27 references.
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- 2026CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents29 Jun
- 2025R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization21 May
Other references · 14
- 2020Estimating means of bounded random variables by bettingcited by 169
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- 2023Generating Synergistic Formulaic Alpha Collections via Reinforcement Learningcited by 39
- 2026Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loopscited by 35
- 2026PACE: Anytime-Valid Acceptance Tests for Self-Evolving Agentscited by 13
- 2026Self-Evolving Agents with Anytime-Valid Certificatescited by 6
- 2024An online generalization of the (e-)Benjamini-Hochberg procedurecited by 4
- 2025Anytime-valid FDR control with the stopped e-BH procedurecited by 3
- 2025Multiple testing in multi-stream sequential change detectioncited by 2
- 2026Improving online FDR procedures via online analogs of e-closure and compound e-valuescited by 2
- 2017Economic Design of Memory-Type Control Charts: The Fallacy of the Formula Proposed by Lorenzen and Vance (1986)cited by 1
- 2026Admissibility and Complete Classes for False Discovery Rate Control with E-valuescited by 1
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