Branch-Solve-Merge (BSM)

Three-module architecture: A branch module proposes a decomposition into parallel sub-tasks, a solve module independently answers each, and a merge module fuses results into a final response.

Prompt-parameterized: All three modules are the same base LLM with different prompts, so BSM works with any base model without fine-tuning.

Evaluation quality gains: Improves evaluation correctness and consistency for multiple LLMs, particularly on tasks where a flat prompt leaves too much implicit.

General pattern: Generalizes the "decompose then solve" pattern from math/CoT to arbitrary tasks, anticipating more structured agent decomposition patterns.