This work explores whether large language models can replicate the qualitative reasoning processes used by investment committees, instead of relying on numerical optimizers.

The first component is a correlation-aware selection method that repurposes a hierarchical clustering dendrogram as a tournament bracket. At each internal node, the LLM allocates selection slots between clusters and performs structured eliminations within correlation regimes.

The second component is a portfolio evolution loop that contains no objective function, expected returns, covariance matrices, or solvers. Instead, the model compares variants using a qualitative rubric (business quality, durability, thematic alignment, drawdown resilience, diversification) and accepts improvements through iterative reasoning.

Both mechanisms are fully text-explainable: every elimination, selection, and mutation is auditable.

Adding a bit of detail: this work tries to replace the standard numerical pipeline (expected returns → covariance → optimizer) with structured reasoning steps.

Two components: • A correlation tree is repurposed as a tournament bracket. At each node, the LLM allocates “selection slots” across branches and performs eliminations inside correlation regimes. • A qualitative evolution loop compares portfolio variants using a rubric (business quality, durability, diversification, resilience) and accepts improvements iteratively — without any explicit optimization objective.

The interesting aspect is not the performance but the explainability: every elimination and mutation step is text-auditable.

Curious whether others have experimented with LLM-based reasoning loops as substitutes for classical optimization in areas outside finance.