Report Contents
- 1Executive Summary & Key Findings
- 2Theoretical Foundations: Chain-of-Thought Reasoning
- 3Beyond Linear: Tree-of-Thoughts & Graph-Based Approaches
- 4Test-Time Compute Scaling (Snell et al. 2025)
- 5Process Reward Models & Verification
- 6Frontier Model Analysis: o3, GPT-5.2, DeepSeek R1
- 7Benchmark Deep Dive: GPQA, ARC-AGI, AIME 2025
- 8Practical Implementation Guidance
- 9Failure Modes & Limitations
- 10Future Directions & Open Problems
What You'll Learn
- How Chain-of-Thought prompting improves reasoning accuracy by 17.9pp on GSM8K
- Why Tree-of-Thoughts outperforms CoT by 18.5x on decomposition tasks
- Test-time compute scaling curves: when to invest in inference vs training
- Process Reward Models vs Outcome Reward Models for verification
- o3's 87.5% ARC-AGI breakthrough and the path to 93.2% GPQA Diamond
- Chain-of-Draft: Achieving 92% token reduction while maintaining accuracy
- LIMO pattern: 57% AIME accuracy with only 817 training samples
- Critical failure modes and when structured reasoning breaks down