These sources provide a comprehensive overview of AI reasoning models, focusing on how they solve complex problems by spending extra "thinking" time during inference. The first source explains that 2026-era models use test-time compute and chain-of-thought processing to explore, verify, and backtrack through logic, making them superior for math and coding despite higher costs and latency. Complementing this, research from Google DeepMind demonstrates these capabilities through AlphaProof and AlphaGeometry 2, which reached a silver-medal standard at the International Mathematical Olympiad by combining reinforcement learning with formal mathematical languages. Finally, a theoretical analysis from MIT and UW-Madison challenges the need for expensive step-by-step human feedback. Their findings suggest that outcome supervision—training based only on final results—is statistically as effective as process supervision for developing advanced reasoning, provided the model has sufficient data coverage. Together, these texts illustrate a shift toward System 2 thinking, where intelligence is scaled not just by model size, but by the deliberate allocation of computational effort during problem-solving.
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