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MARS Strategy Network

The strategy network enables collective learning from solutions.

Overview

The strategy network:

  • Extracts successful reasoning patterns
  • Shares strategies between agents
  • Learns from verified solutions
  • Improves future generations

Strategy Extraction

From a good solution, extract:

Rust
pub async fn extract_strategies(
    solution: &Solution,
    client: &dyn ModelClient,
) -> Result<Vec<String>> {
    // Identify key techniques
    // List step-by-step approaches
    // Extract domain-specific patterns
}

Example extraction:

Text Only
Solution: "First, decompose into subproblems. Then solve each
          independently. Finally, combine results."

Extracted Strategies:
  1. Decompose complex problems into subproblems
  2. Solve subproblems independently
  3. Combine results systematically

Strategy Integration

Strategies are integrated into system prompts for future agents:

Text Only
Base System Prompt:
  "You are a helpful assistant."

Enhanced with Strategies:
  "You are a helpful assistant. When solving problems:
   - Decompose complex problems into subproblems
   - Solve subproblems independently
   - Combine results systematically"

Strategy Network Architecture

Text Only
Agent 1 Solution
    ↓
Strategy Extraction
    ↓
Extracted Strategies
    ↓
Agent 2 & 3 System Prompt
    ↓
Improved Solutions

Collective Learning

Over iterations:

  • Round 1: Agents generate diverse solutions
  • Round 2: Extract strategies from best solutions
  • Round 3: New agents use extracted strategies
  • Round 4: Further refinement

Configuration

Strategy network behavior is configured via:

Rust
pub struct StrategyNetworkConfig {
    pub enabled: bool,
    pub max_strategies_per_round: usize,
    pub strategy_weight: f32,  // 0.0-1.0
}

Performance Impact

With Strategy Network: - Better solutions over time - Cumulative improvement - Longer overall time

Without Strategy Network: - Faster individual rounds - No cumulative improvement - Simpler system

Limitations

  • Some strategies may not generalize
  • Over-specialization risk
  • Added latency from extraction

Best Practices

  1. Verify before using: Only use strategies from verified solutions
  2. Diversity: Keep diverse strategies, not just best
  3. Rotation: Retire old strategies periodically
  4. Validation: Test strategies on diverse problems

See MARS Overview and Agent System.