Recently, the SAT Competition 2026, the leading international competition in the field of constraints and formalization, has come to a head. A joint team composed of the Huawei Noah Ark Laboratory, the Huawei Cloud Fund AI Resolver Team and the John Hopcroft Computing Center at the Chinese University of Technology and Technology, has won the SAT group championship in parallel AI races.
The SAT problem is known as the pinnacle of computational complexity theory, from hardware verification, software testing to cryptographic analysis and AI planning, the SAT solver has become an indispensable core engine in industrial research and development such as autonomous driving, EDA chip design. The SAT Competition is the most authoritative international competition in this field, organized by the SAT Conference, and has been around for more than two decades. The SAT Competition, which brings together the world's top scientific research institutions and enterprise teams to compete, often becomes the standard tool for academia and industry, and is used by top international companies directly into the production environment.
The 2026 SAT Competition attracts 45 of the world's top teams, and for the first time, an AI racetrack is set up that requires AI-optimized solvers to exceed non-AI solvers in performance to win prizes. This means that AI must not only serve as an auxiliary optimization, but also bring real, quantifiable, verifiable algorithmic performance improvements, marking the SAT competition's move from artificial algorithm design to a new phase of AI-classic algorithms + AI-integrated innovation.
The championship, based on Huawei's cloud computing decision-making intelligent engine and AI algorithm automation design technology, built on the SAT solver's proprietary algorithm optimization pipeline, integrates core capabilities such as expert knowledge injection, multi-smart collaboration, experience-efficient sampling, multi-dimensional multi-dimensional algorithm evaluation, relies on distributed computing clusters to achieve high-speed algorithm evaluation scores, and opens the entire code warehouse automation algorithm design chain. This means that AI is no longer merely a parameter proponent or auxiliary development tool, but is deeply involved in solver's algorithm generation, coding evolution, performance evaluation and strategy optimization processes, focusing on all kinds of unseen algorithm data, achieving stability, and improving the combined performance.
Automated algorithm construction: the platform relies on large model-driven algorithms for self-exploration, natural language input, algorithm conception, code generation, iterative screening and performance evaluation, without the need to rely on man-made complete algorithm logic. The system is combined with evolutionary search, continuous generation of candidate programs, automatic elimination of ineffective versions. In this SAT competition, this capability will be used to automate the evolution of SAT solver strategies and explore solutions that are more than man-made.
Independent memory management: the platform features Huawei's self-developed memory management platform "MindMemOS", which is able to store task inspiration, project scenario precedents and cross-task general algorithm paradigms, success and failure lessons in the process of embedding test errors, avoid repeated cratering, allow algorithm design from single-search craters to continuous accumulation craters. For example, in the SAT solver iteration process, the system records the advantages of optimization strategies in different instances, and subsequent encounters of similar problems can be directly used historical experience, reducing repeated evaluation expense.
Skill lightweight deployment: the platform will pack verified algorithms, algorithm modules into replicable skill units, subsequent tasks can be called directly, rearranged components, without having to construct the entire code from scratch every turn. As the project continues to evolve, the skill library will continue to expand, large models can be deployed based on existing components to innovate and double-optimize, effectively reduce the complexity of algorithm generation, shorten algorithm development cycles, and improve the quality and stability of algorithm output under new tasks. Skill chemistry currently supports 10 types of automated algorithm design methods.
Autonomous research platform: Based on the aforementioned capabilities, the platform opens up a complete scientific research linkage from problem definition, algorithm search, benchmark contrast, result attribution, and paper writing. This capability is based on the 23rd phase of the open source autonomous scientific research intelligent body AutoResearchClaw's end-to-end scientific research pipeline, and targeted improvements are made to its shortcuts in the algorithm design. LLM4AD Next is integrated into AutoResearchClaw, and before coding, let LLM4AD_Next try to improve the core algorithms in it, then use the original experiment evaluation method one by one.
The platform, which is aimed at university students, researchers and large algorithm developers, allows more people to use this set of tools to allow algorithms to evolve themselves and turn ideas into verifiable results better, faster, and more easily.
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