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AutoThink Strategy

Overview

AutoThink is a complexity-aware optimization strategy that analyzes query characteristics and automatically selects optimal parameters based on problem difficulty.

Key Features

  • Multi-factor Analysis: Analyzes length, vocabulary, reasoning keywords, domain indicators, and structure
  • Adaptive Temperature Selection: Scales from 0.3 (simple) to 1.0 (complex)
  • Complexity Classification: Categorizes queries as Simple, Medium, or Complex
  • Efficient: Single-pass analysis with no additional API calls

How It Works

AutoThink uses a 5-factor difficulty estimation algorithm:

  1. Length Analysis (20%): Word count ranges indicate complexity
  2. 0-10 words: Simple
  3. 11-30 words: Somewhat simple
  4. 31-70 words: Medium
  5. 71-150 words: More complex

  6. 150+ words: Very complex

  7. Vocabulary Complexity (25%): Analyzes word sophistication

  8. Advanced vocabulary detection (theorem, methodology, optimization)
  9. Average word length (longer = more complex)

  10. Domain-specific jargon (matrix, integral, algorithm)

  11. Reasoning Keywords (25%): Weighted keyword detection

  12. High weight (1.0): prove, derive, recursive
  13. Medium weight (0.8-0.9): analyze, optimize, design

  14. Lower weight (0.6-0.7): explain, compare, solve

  15. Domain Indicators (15%): Identifies specialized domains

  16. Mathematical: calculus, algebra, topology
  17. Programming: algorithms, data structures
  18. Physics: quantum, relativity, mechanics

  19. Logic: proof, axiom, theorem

  20. Structural Complexity (15%): Analyzes query structure

  21. Multi-question queries
  22. Nested structures (parentheses, brackets)
  23. Sentence length
  24. Separators (colons, semicolons)

Configuration

Rust
let config = AutoThinkConfig {
    simple_token_threshold: 50,
    complex_token_threshold: 150,
    simple_temperature: 0.3,
    medium_temperature: 0.6,
    complex_temperature: 1.0,
};

Temperature Selection

Temperature automatically scales based on complexity:

Complexity Temperature Use Case
Simple 0.3 Factual questions, definitions, straightforward problems
Medium 0.6 Mixed reasoning, some exploration needed
Complex 1.0 Proofs, algorithm design, theoretical problems

Advantages

  • Intelligent Parameter Selection: No need to manually tune temperature
  • Domain Aware: Recognizes mathematical, programming, and scientific problems
  • Efficient: Lightweight analysis, no additional model calls
  • Comprehensive: Considers multiple difficulty signals

Use Cases

  • Mathematical problem-solving
  • Algorithm design and optimization
  • Theory proofs and derivations
  • Complex system analysis
  • Research question answering

Examples

Simple Query

Text Only
"What is the capital of France?"
→ Complexity: Simple (0.2)
→ Temperature: 0.3 (deterministic)

Medium Query

Text Only
"Explain how quicksort works and analyze its time complexity"
→ Complexity: Medium (0.5)
→ Temperature: 0.6 (balanced)

Complex Query

Text Only
"Prove that the Riemann Hypothesis is true or provide a counterexample using mathematical rigor"
→ Complexity: Complex (0.85)
→ Temperature: 1.0 (exploratory)

Performance Tips

  • Works best for academic and technical queries
  • Vocabulary analysis may be less effective for non-English queries
  • Domain detection optimized for math, CS, physics, logic
  • Can be combined with other strategies for enhanced results

References

  • AutoThink Paper: Complexity-based Adaptive Reasoning (forthcoming)
  • Related: Self-Consistency, Best-of-N