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Updated August 2026 · 7 min read · by the ADHD project Direct answer: Choose by problem shape. Chain-of-Thought (CoT) wins on problems with one correct answer — math, logic, step-by-step derivation. Tree-of-Thought (ToT) wins when you must search a space and backtrack — planning, puzzles. Parallel divergent ideation wins on open-ended problems with many viable answers — design, naming, strategy — because it is the only one of the three that eliminates anchoring instead of managing it.

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The three techniques at a glance

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Chain-of-Thought: one head thinking slower

CoT prompts the model to reason step by step before answering. It reliably improves accuracy on math, logic, and multi-step derivation — and modern reasoning models bake it in as extended thinking. Its limit: every step is conditioned on the previous ones, so by step 4 the chain has committed to a framing. For questions with one right answer that’s a feature; for open questions it’s the failure mode. Use CoT when: the problem has a verifiable correct answer and the risk is calculation error, not framing error.

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Tree-of-Thought: one head searching wider

ToT expands multiple candidate next steps, evaluates them, and backtracks — a search tree over reasoning states. It shines on puzzles and planning problems where the path matters. But the tree is typically walked inside one shared session: branches see each other’s traces, so the anchoring of the first expansions persists across the whole tree. ToT varies the next move; it doesn’t vary the question’s framing. Use ToT when: you’re searching for a path — game states, constraint puzzles, multi-step plans with dead ends.

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Parallel divergent ideation: many heads, then an editor

For open-ended problems, the bottleneck is not path-finding — it’s that all candidates come from one anchored generator. Parallel divergent ideation spawns N isolated branches (separate API calls, zero shared context), each re-posing the whole question from a different cognitive frame, then runs a separate critic call to score, cluster, prune traps, and deepen the best ideas. The three load-bearing differences from CoT/ToT:
  1. Isolation, not search — branches never see each other, so anchoring is eliminated by construction.
  2. Frames, not next-step variation — each branch re-asks the entire question (“as an immune system”, “as a regulator”), producing structurally different ideas rather than nearby ones.
  3. A mechanical generator/critic split — divergence is a call that forbids evaluation; convergence is a separate call that requires it.
One-sentence version: CoT makes one head think slower. ToT makes one head search wider. Parallel ideation makes many heads think differently, then has a critic pick. The ADHD skill implements this loop for coding agents (how it works). In evals across six open-ended engineering problems it beat single-shot prompting on breadth (9.00 vs 4.83), novelty (7.83 vs 2.67), and trap detection (9.50 vs 1.83) — methodology and limitations here.

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Decision guide

  • “What’s the time complexity of this algorithm?” → direct answer (no reasoning needed)
  • “Prove this invariant holds” → CoT / extended thinking
  • “Find a sequence of moves that solves this” → ToT
  • “How should we shard this queue under bursty load?” → parallel divergent ideation
  • “Name this product” / “design this API surface” → parallel divergent ideation

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Frequently asked questions

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Is parallel divergent ideation just Tree-of-Thought with extra steps?

It’s a ToT variant with two structural changes: branching is driven by cognitive frames rather than next-step variation, and the generator/critic split is enforced by separate API calls rather than promised in one prompt. Those two changes are what eliminate anchoring — the thing in-context ToT can’t do.

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Can I combine them?

Yes, and you should. Diverge in parallel to map the option space, then apply CoT/extended thinking inside the deepening pass on the top candidates. That’s exactly the shape of ADHD’s focus phase.

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Which is cheapest?

CoT (1 call) < ToT (a few calls) < parallel ideation (~10 calls at defaults). Match spend to stakes: reasoning on a lookup is waste; a single shot on an architecture decision is false economy. See the cost model.
Further reading: ADHD vs CoT & ToT — full comparison · When to use ADHD