Branch-Solve-Merge (BSM)
BSM decomposes LLM tasks into parallel sub-tasks via three LLM-programmed modules: branch, solve, and merge.
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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.