ByteDance's Seed-Prover Solves Four IMO 2024 Problems, Challenging DeepMind in Formal Reasoning

The pursuit of artificial general intelligence has increasingly focused on mathematical reasoning as a proxy for an AI model's ability to plan, verify, and execute complex logic—often referred to as "System 2" thinking. ByteDance has entered this rapidly developing sector with the release of Seed-Prover, a system built on the Lean v4.14.0 programming language. According to the technical release, the system successfully solved problems P2, P3, P4, and P5 of the IMO 2024, a performance that rivals top-tier proprietary models currently under development by Western competitors.

Technical Performance and Novelty

The Seed-Prover system demonstrated significant versatility across different mathematical domains, including geometry, number theory, and algebra. In geometry, the system exhibited high-speed verification capabilities; the proof for Problem 2 (P2) was generated and verified in only two seconds. This speed suggests that the model has optimized search heuristics for geometric constraints, a historically difficult area for text-based large language models (LLMs).

However, the system's robustness was tested more rigorously in number theory. For Problems 3 and 4, the formalization required substantial output, necessitating 2,000 and 4,000 lines of Lean code respectively. This volume of code indicates that while the system can solve complex problems, the path to the solution in a formal language remains verbose, potentially highlighting an area for future optimization in code efficiency.

Perhaps the most significant qualitative finding was the system's performance on Problem 5 (P5), a combinatorics/algebra challenge. The Seed Team noted that the algorithm generated a proof that "differed significantly from traditional human solutions". This deviation suggests that the model is not merely retrieving memorized patterns from training data but is exploring novel solution spaces—a characteristic previously observed in DeepMind's AlphaGo and AlphaGeometry.

Open Source Strategy and Competitive Landscape

Unlike Google DeepMind, which has kept the weights and full architecture of systems like AlphaProof largely proprietary, ByteDance has opted for an open-source approach. The Seed-Prover has been released under an Apache-2.0 license, allowing the broader research community to inspect, reuse, and build upon the architecture. This strategy may accelerate the commoditization of formal theorem proving, lowering the barrier to entry for researchers who are not fluent in the Lean formalization language.

This release places ByteDance in direct competition with established players. DeepMind’s success at IMO 2024 set the initial benchmark, and the Seed-Prover's ability to tackle four problems from the same competition suggests the gap is narrowing. By utilizing Lean v4.14.0, ByteDance is leveraging a rigorous verification environment that eliminates the "hallucination" problem common in standard LLMs; if the code compiles in Lean, the math is valid.

Limitations and Strategic Unknowns

Despite the success, the disclosure leaves critical questions unanswered regarding the system's limitations. The report notably omits the status of Problem 1 (P1) and Problem 6 (P6). In the context of the IMO, P1 is typically the most accessible problem, while P6 is traditionally the most difficult. The omission of P1 is anomalous and could imply a failure in handling specific "easy" problem types or a parsing error, while the absence of P6 suggests the system has not yet reached the ceiling of human mathematical capability.

Furthermore, the architecture powering the "Delta-Prover" component and the exact inference costs remain opaque. It is unclear whether the system operated fully autonomously during the simulation or if there was human intervention involved in translating natural language problems into Lean formalization. As the industry pivots toward reasoning-heavy models, the efficiency of test-time compute will be as critical as the solve rate.

ByteDance’s entry demonstrates that the capability to automate high-level mathematical reasoning is not the exclusive domain of Silicon Valley. As formal verification becomes a standard for evaluating model reliability, tools like Seed-Prover will likely serve as the foundation for the next generation of trustworthy AI systems.