| name | tree-of-thoughts |
| description | Multi-path reasoning with evaluation and backtracking - 74% success on complex tasks |
| trigger | complex reasoning, planning, multi-step problems |
| priority | 2 |
| dynamic | false |
| created | 2026-01-26 |
Tree of Thoughts (ToT) Pattern
Multi-path reasoning that explores, evaluates, and backtracks. Achieved 74% success rate on Game of 24 vs 4% with standard CoT.
Activation
Triggers on:
- Problems requiring non-trivial planning
- Decision trees with multiple valid paths
- Complex debugging with multiple hypotheses
- Creative tasks with evaluation criteria
Process
Phase 1: Decompose
Break problem into intermediate steps that can be evaluated independently.
Phase 2: Generate (Branch)
At each step, generate 3 candidate "thoughts" (reasoning paths):
STEP N:
├─ Thought A: [approach 1]
├─ Thought B: [approach 2]
└─ Thought C: [approach 3]
Phase 3: Evaluate
Score each thought (0-10) on:
- Progress toward goal
- Feasibility
- Reversibility if wrong
Phase 4: Search Strategy
- BFS: Explore all branches at depth N before N+1 (shallow trees)
- DFS: Follow promising paths deeper (deep trees)
- Beam Search: Keep top-K paths at each level
Phase 5: Backtrack
If path scores drop below threshold (5/10), backtrack to last good node.
Zero-Shot ToT Prompt
Use this simple prompt for ToT without explicit tree construction:
"Imagine three different experts are answering this question.
All experts will write down 1 step of their thinking, then share it with the group.
Then all experts will go on to the next step.
If any expert realizes they're wrong at any point, they leave.
Continue until consensus."
Integration
- Works with @architect for system design decisions
- Pairs with @debugger for multi-hypothesis testing
- Feed final path to learning engine for pattern storage
Based on Princeton/DeepMind research - arXiv:2305.10601
