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.
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.