Evaluation and decision support
- Application
How to evaluate research systems, and why professionals keep the decision.
Research earns its keep when it changes a decision. That raises the bar for evaluation: a persuasive report and a conclusion that deserves to be acted on can look alike, and only measurement tells them apart.
State of the field
Verification has become the hard part. At Jane Street, Ron Minsky observes that models can seem to do more work than they really do “because this verification piece is as hard as it ever was. In fact, maybe harder”, and the team now writes evaluations for the model graders it uses (Minsky and Crepezzi, 2026 (external site)).
Models are often used to grade other models. Strong judges agree with human judges more than 80% of the time, but they also favor longer answers, certain positions, and their own output (Zheng et al., 2023 (external site)). Anthropic recommends deterministic graders where possible, model graders where necessary, and human graders “judiciously for additional validation”, and, because model outputs vary between runs, running multiple trials (Anthropic, 2026 (external site)).
Firms that use these systems in investing stay cautious. Man Group holds AI-generated signals to the same thresholds as human research and writes that “we can’t leave it unsupervised just yet” (Fang and Moore, 2025 (external site)). The NIST AI Risk Management Framework (external site) (NIST, 2023) gives organizations a voluntary structure for that kind of oversight.
Principles
Decisions belong to the professionals who act on them. A research system should hand them a clear conclusion with the reasoning, the evidence, the risks, and the open questions laid out beside it, so they can disagree with it for good reasons. Nothing we publish is investment advice; our tools are built to support judgment.
Our approach
We aim to measure research quality against outcomes and expert review, and to keep a short, documented path from a finished analysis to a considered action, with the decision left to professionals.
References
- Ron Minsky and John Crepezzi, Jane Street (2026). The Changing Craft of Programming (external site). Signals and Threads, episode 31.
- Lianmin Zheng et al. (2023). Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena (external site). arXiv:2306.05685.
- Anthropic (2026). Demystifying evals for AI agents (external site).
- Ziang Fang and Jason Moore, Man Group (2025). What AI Can (and Can’t Yet) Do for Alpha (external site).
- National Institute of Standards and Technology (2023). AI Risk Management Framework (external site).