Navier-Stokes and other frontier math discoveries have shown that multi-agent ensembles are required to escape the typical creativity constraints of LLMs. Is this also applicable to algorithmic trading research, which similarly lacks direct answers in LLM training data? The general method for producing a proof of an unsolved math problem has been to set up large teams of agents to - create multiple formations of the problem - selectively research or exclude known results - search for counterexamples or use counterexample attempts to tighten problem bounds - randomly apply unrelated areas of mathematics - use formal verification methods to check other agents’ work - project manage and referee across all existing agents Single-agent systems tend to plateau to a mean knowledge level. This covers the majority of tasks at an efficient cost per token, as the answers to most queries lie explicitly in the latent space of LLMs. Clear examples where single agents fall short are complex system design and numerical analysis, both of which describe algorithmic trading research. The ensemble method has potential to discover new alpha in algorithmic trading, but at the cost of substantial compute and tokens. Frontier model companies won the competition for quant/engineering talent against large trading firms a few years ago, but are not yet deploying them in this direction.