tree-of-thoughts

Execute complex reasoning tasks through systematic exploration of solution space, pruning unpromising branches, expanding viable approaches, and synthesizing the best solution.

• Purpose - Explore multiple solution paths before committing to full implementation

• Pattern - Tree of Thoughts (ToT) with adaptive strategy selection

• Output - Superior solution combining systematic exploration with evidence-based synthesis

• Efficiency - 15-20% average cost savings through adaptive strategy (polish clear winners, redesign failures)

Quality Assurance

Enhanced verification with probability estimates, meta-judge evaluation specifications, multi-stage evaluation, and adaptive strategy

Pattern: Tree of Thoughts (ToT)

This command implements an eight-phase systematic reasoning pattern with meta-judge evaluation and adaptive strategy selection:

Phase 1: Exploration (Propose Approaches)
         ┌─ Agent A → Proposals with probabilities ─┐
Task ───┼─ Agent B → Proposals with probabilities ─┼─┐
         └─ Agent C → Proposals with probabilities ─┘ │
                                                       │
Phase 1.5: Pruning Meta-Judge (parallel with Phase 1) │
         Meta-Judge → Pruning Evaluation Spec YAML ───┤
                                                       │
Phase 2: Pruning (Vote for Best 3)                    │
         ┌─ Judge 1 → Votes + Rationale ─┐            │
         ├─ Judge 2 → Votes + Rationale ─┼────────────┤
         └─ Judge 3 → Votes + Rationale ─┘            │
                 │                                     │
                 ├─→ Select Top 3 Proposals            │
                 │                                     │
Phase 3: Expansion (Develop Full Solutions)           │
         ┌─ Agent A → Solution A ─┐                   │
         ├─ Agent B → Solution B ─┼───────────────────┤
         └─ Agent C → Solution C ─┘                   │
                                                       │
Phase 3.5: Evaluation Meta-Judge (parallel w/ Phase 3)│
         Meta-Judge → Evaluation Spec YAML ───────────┤
                                                       │
Phase 4: Evaluation (Judge Full Solutions)            │
         ┌─ Judge 1 → Report 1 ─┐                     │
         ├─ Judge 2 → Report 2 ─┼─────────────────────┤
         └─ Judge 3 → Report 3 ─┘                     │
                                                       │
Phase 4.5: Adaptive Strategy Selection                │
         Analyze Consensus ───────────────────────────┤
                ├─ Clear Winner? → SELECT_AND_POLISH  │
                ├─ All Flawed (<3.0)? → REDESIGN      │
                └─ Split Decision? → FULL_SYNTHESIS   │
                                         │             │
Phase 5: Synthesis (Only if FULL_SYNTHESIS)           │
         Synthesizer ────────────────────┴─────────────┴─→ Final Solution

Usage

# Basic usage
/tree-of-thoughts <task-description>

# With explicit output specification
/tree-of-thoughts "Design authentication middleware" --output "specs/auth.md"

# With specific evaluation criteria
/tree-of-thoughts "Design caching strategy" --criteria "performance,memory-efficiency,simplicity"

When to Use

✅ Use ToT when:

• Solution space is large and poorly understood

• Wrong approach chosen early would waste significant effort

• Task has multiple valid approaches with different trade-offs

• Quality is more important than speed

• You need to explore before committing

❌ Don't use ToT when:

• Solution approach is obvious

• Task is simple or well-defined

• Speed matters more than exploration

• Only one reasonable approach exists

Quality Enhancement Techniques

Phase	Technique	Benefit
Phase 1	Probabilistic Sampling	Explorers generate approaches with probability estimates, encouraging diversity
Phase 1.5	Pruning Meta-Judge (sadd:meta-judge)	Generates tailored rubrics and scoring criteria before pruning judges evaluate
Phase 2	Multi-Judge Pruning	Independent judges apply meta-judge specs to vote on top 3, reducing groupthink
Phase 3	Feedback-Aware Expansion	Expanders address concerns raised during pruning
Phase 3.5	Evaluation Meta-Judge (sadd:meta-judge)	Generates evaluation specification before full-solution judges evaluate
Phase 4	Structured Evaluation	Judges apply meta-judge-generated criteria with explicit evidence, reducing bias
Phase 4.5	Adaptive Strategy Selection	Orchestrator parses structured outputs to select optimal strategy
Phase 5	Evidence-Based Synthesis	Combines proven best elements rather than creating new solutions

Theoretical Foundation

Based on:

• Tree of Thoughts (Yao et al., 2023) - Systematic exploration and pruning

• Self-Consistency (Wang et al., 2023) - Multiple reasoning paths

• Constitutional AI (Bai et al., 2022) - Critique and refinement

• LLM-as-Judge (Zheng et al., 2023) - Multi-perspective evaluation

• Chain-of-Verification (Dhuliawala et al., 2023) - Structured verification reduces bias