The research review includes sources available through 9 October 2026. The guides include optional challengers: causal, generative recommendation, inventory-learning and survival foundation models. Being new does not make them the best starting models. Some are recent preprints or workshop results. Industrial engagement results also answer a different question from incremental business contribution.
Promote a candidate only after it clears these comparisons:
- Task and evidence: match the prediction, treatment, censoring and deployment setting to yours. Write the identifying assumptions before fitting.
- Reproduction: check the release, licence, preprocessing, tuning budget and evaluation split. Compare on the same data, action support, hardware and allowed candidate count.
- Decision value: measure calibrated risks or effect sizes, supported policy value and uncertainty, as well as ranking. Include no action and a simple nonpersonalised policy.
- Operation: test cold start, missing inputs, drift, inference cost, latency, constraints and fallback. A research benchmark says little about reliability in production.
The practical starting point remains a transparent baseline and a calibrated boosted-tree challenger for tabular prediction, identified causal estimation for interventions, and an explicit constrained decision policy. Optional research comparisons sit outside the prototype time budget.