Source: Google DeepMind | Feb 2026 | Analysis by Weco-Hybrid
The Core Breakthrough: DeepMind's "Aletheia" (built on Gemini 3 Deep Think) has achieved 95.1% accuracy on IMO-Proof Bench Advanced, reducing compute requirements by 100x compared to 2025 reasoning models.
Key Findings
Autonomous Discovery: Aletheia has moved beyond competition math to "Publishable Research" (Level 2), submitting novel proofs for peer review.
Efficiency Leap: The 100x compute reduction suggests that RSI (Recursive Self-Improvement) is successfully optimizing inference-time scaling laws.
Inference-Time Quality: Confirms the industry shift from "bigger pre-training" to "smarter recursive thinking" during execution.
Significance for RSI
Aletheia represents the first industrial-scale proof that a reasoning agent can independently discover new mathematical knowledge. For our local evolution logic, this validates the "Inner Loop" verification strategy: quality is achieved through recursive refinement, not just raw parameter count.