On December 24, 2025, ByteDance’s Seed team unveiled Seed Prover 1.5, a specialized AI model for formal mathematical theorem proving. The system generated Lean proofs for five IMO 2025 problems and 11 of 12 Putnam 2025 problems within hours, setting new benchmark scores on multiple math reasoning test sets.
This article aggregates reporting from 4 news sources. The TL;DR is AI-generated from original reporting. Race to AGI's analysis provides editorial context on implications for AGI development.
Seed Prover 1.5 is one of the clearest signals yet that frontier AI is moving from informal “show your work” math to fully formalized, machine-checkable reasoning. By hitting gold-medal-equivalent scores on IMO 2025 problems and solving the vast majority of Putnam 2025 questions in Lean, ByteDance is demonstrating that reinforcement‑trained, agentic systems can navigate deep mathematical search spaces with reliability, not just eloquent speculation.([finance.sina.com.cn](https://finance.sina.com.cn/tech/digi/2025-12-24/doc-inhcwnvm5989887.shtml?utm_source=openai))
For the race to AGI, this matters because formal theorem proving is a brutal test of compositional reasoning, error correction, and long-horizon planning. Techniques like large‑scale “agentic RL” and test‑time scaling that work here are highly portable to safety‑critical domains such as code verification, scientific discovery, and even formalizing AI safety proofs. The fact that this work is coming out of ByteDance, not a Western lab or Big Tech cloud provider, underlines how multi‑polar advanced reasoning research has become.([php.cn](https://www.php.cn/faq/1887961.html?utm_source=openai))
Strategically, Seed Prover 1.5 raises the bar for everyone working on mathematically grounded AI. It will push rivals like OpenAI, Google DeepMind, and Anthropic to respond with their own formal reasoning systems, feeding a virtuous (and potentially risky) cycle where models learn to reason not just better, but with guarantees. As more of this tech moves behind APIs rather than just papers, it edges formal reasoning from a niche research topic toward a platform capability that could underpin future AGI architectures.


