When language intelligence is not enough.

Start with the output, not the AI label.

If the desired output is an explanation, translation, summary or conversational interface, an LLM may be the right primary model. If the desired output is a forecast, probability distribution, simulation, risk number, optimized allocation or quantitative decision, the architecture should make the numerical method explicit.

Six signals that you need more than an LLM

1. The output will be scored against reality. Forecasting and prediction need error metrics, calibration and outcome tracking.
2. The output changes money, capacity or risk. Capital allocation, pricing, inventory and operational decisions need explicit numerical boundaries.
3. You need reproducible scenarios. Simulations need assumptions, distributions, seeds and traceable parameters.
4. You need constraints. Optimization requires objective functions and feasible sets, not persuasive prose.
5. You need model governance. Institutional approval needs a versioned model identity and validation record.
6. You need execution evidence. A result that matters should retain enough lineage to show how it was produced.

The replacement is not “one giant math model.”

LargeQuant uses LQM as an operating category. A governed LQM can be statistical, econometric, algorithmic, tree-based, neural, simulation-based, optimization-based or composed from several methods. What matters is that quantitative authority is explicit and measurable.

Language can remain the interface.

An LLM can still interpret a request, choose an approved tool, explain a result and help a user navigate evidence. The important boundary is that the language layer should not silently replace the quantitative system when numerical correctness is required.