II · Planning & Control FlowMature★★

Multi-Path Plan Generator

also known as Branching Plan Generator, Candidate-Path Producer

Generate multiple candidate next-steps at each plan node enabling later selection — the planning generator pattern paired with tree-of-thoughts / LATS-style search.

Context

A team uses tree-of-thoughts or LATS for plan search. The generator step that produces candidate next-steps is often conflated with the search policy. Naming the generator separately allows mixing different generators with different search policies.

Problem

When generator and search policy are fused, neither can be tuned independently. The generator's quality limits the search; the search's strategy limits how generator candidates are used. Isolating the generator (this pattern) from the search policy enables independent tuning. Distinct from single-path-plan-generator and from tree-of-thoughts (the full search algorithm).

Forces

  • Generator and search policy are often described together, making them hard to swap.
  • Multi-path generators are expensive — N candidate steps per node.
  • Quality of candidates depends heavily on generator design.

Example

A code-refactoring agent uses LATS search. Generator: at each node it produces K=4 candidate refactoring approaches (rename, extract method, inline, restructure). Search policy: evaluates expected reward per candidate (heuristic + LLM scorer) and expands the top 2. Generator and search policy are separate; team experiments with generator quality (different prompts) without touching search policy.

Diagram

Solution

Therefore:

Multi-path generator interface: (current_node, history, K) → [candidate_step_1, ..., candidate_step_K]. Search policy (tree-of-thoughts, LATS, beam search, MCTS) decides which candidates to expand. Generator and search policy are separate components and can be swapped independently. Pair with tree-of-thoughts, lats, single-path-plan-generator (alternative), beam search.

What this pattern forbids. The generator produces K candidates and does not decide which to expand; search policy is a separate component.

And the patterns that stand alongside it, or against it —

  • complementsTree of Thoughts★— Search over a tree of partial reasoning states with explicit lookahead, evaluation, and backtracking.
  • complementsLanguage Agent Tree Search·— Lift the agent loop into a search tree with a learned value function and backtracking.
  • alternative-toSingle-Path Plan Generator★★— Generate one linear sequence of intermediate steps from current state to goal — the lightweight planning alternative to tree-of-thoughts and multi-path generation.
  • complementsBest-of-N Sampling★— Sample N candidate outputs and select the highest-ranked by a reward model or scorer.
  • complementsAdaptive Branching Tree Search·— At each node of an inference-time search tree, use Thompson sampling to decide whether to deepen an existing answer or branch a fresh attempt, optionally choosing per-node which underlying LLM to invoke.
  • complementsIncremental Model Querying★★— Generate plan steps by sequentially querying the model at each step rather than producing the whole plan upfront in one call.
  • complementsGenerate-and-Test Strategy★— Generate multiple candidate solutions in parallel, then systematically test each against declared constraints rather than committing to the first plausible one — adapted from Langley & Simon's cognitive-science research on human expert problem-solving.

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References

Provenance