官方技术资料：Google / DeepMind（第 9 页）
官方技术资料 · Google / DeepMind：共 135 条，本页为第 121–135 条。
AlphaZero self-play reinforcement algorithm
Self-taught reinforcement learning algorithm that mastered chess, shogi, and Go starting from scratch through self-play. It combines deep neural networks with Monte Carlo Tree Search (MCTS) to evaluate positions, replacing human-engineered heuristics and brute-force search databases with learned strategic intuition.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：reinforcement-learning · model-selection
链接复核时间：2026-09-24
MuZero model-based planning algorithm
Model-based reinforcement learning algorithm that learns to master complex environments—including chess, shogi, Go, and Atari—without being programmed with the underlying rules. It achieves this by modeling only the variables critical to planning and decision-making: the value of the current state, the optimal policy to take, and the immediate reward.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：reinforcement-learning · model-selection
链接复核时间：2026-09-24
AlphaFold protein structure prediction model
Deep learning architecture engineered to predict three-dimensional protein structures and biomolecular complexes from primary amino acid sequences. Connects to the AlphaFold Protein Structure Database and AlphaFold Server to provide structural data maps and interaction modeling tools for non-commercial scientific research.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：neural-network · deployment
链接复核时间：2026-09-24
WeatherNext atmospheric forecasting model
Global medium-range atmospheric forecasting model family built on a Functional Generative Network (FGN) architecture. Computes 15-day global trajectory ensembles on a 0.25° spatial grid with up to 1-hour temporal resolution, predicting core atmospheric and surface variables to track low-probability extreme weather events and rapid intensification.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：neural-network · deployment
链接复核时间：2026-09-24
AlphaGenome DNA sequence model
Unifying DNA sequence model that predicts how genetic variants and mutations affect regulatory activity across coding and non-coding regions. It processes long sequence context up to 1 million base-pairs at single-base resolution, mapping molecular properties such as RNA splicing, gene boundaries, and accessibility to accelerate research in disease biology and synthetic genome design.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：neural-network · deployment
链接复核时间：2026-09-24
AlphaEarth Foundations geospatial AI model
Geospatial AI foundation model designed to synthesize multi-source Earth observation data (including optical, radar, 3D laser mapping, and climate datasets) into compact 64-dimensional embeddings at a 10x10 meter resolution. Integrated into Google Earth Engine as the Satellite Embedding dataset, it enables planetary-scale tracking of environmental, agricultural, and ecological shifts with reduced computational storage overhead.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：neural-network · deployment
链接复核时间：2026-09-24
GNoME crystal structure prediction framework
Deep learning framework based on Graph Neural Networks (GNNs) designed to predict the stability and structure of inorganic crystalline materials. Powered by an active learning pipeline validated with Density Functional Theory (DFT) into its training dataset, it discovered 2.2 million new crystal structures, contributing 380,000 highly stable candidate materials to the Materials Project to assist in the experimental synthesis of superconductors, solid-state batteries, and electronics.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：neural-network · deployment
链接复核时间：2026-09-24
AlphaQubit quantum error correction decoder
AI-based quantum decoder designed to identify and correct physical errors in superconducting quantum processors. Utilizing deep learning architectures trained on data from Sycamore processors, it analyzes experimental measurement histories to classify error events (bit-flips and phase-flips) with high accuracy, outperforming traditional algorithmic decoders to assist in scaling stable logical qubits.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：neural-network · deployment
链接复核时间：2026-09-24
Fluid Dynamics PINN discovery framework
Mathematical discovery framework based on Physics-Informed Neural Networks (PINNs) designed to systematically identify unstable singularities ("blow ups") in complex fluid equations. By training neural networks directly on physical laws using second-order optimizers to achieve near-machine precision, it maps mathematical patterns in singularity speed and instability to assist in computer-assisted proofs and fluid mechanics research.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：neural-network · deployment
链接复核时间：2026-09-24
AlphaEvolve algorithm discovery agent
Evolutionary coding agent powered by Gemini models (utilizing Gemini Flash for exploration breadth and Gemini Pro for analytical depth) designed for general-purpose algorithm discovery and optimization. By combining LLM-driven codebase generation with automated execution and verification loops, it iteratively refactors complex algorithms to optimize digital infrastructure (such as GPU kernels, TPU chip architectures, and data center scheduling) and solve open mathematical problems.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：agent · code-execution
链接复核时间：2026-09-24
AlphaProof mathematical reasoning model
Advanced reasoning model configuration designed for complex, multi-step problem solving, demonstrated by achieving a certified gold-medal standard score at the 2025 International Mathematical Olympiad (IMO). Operating end-to-end in natural language within a standard 4.5-hour time limit, it utilizes parallel thinking architectures to simultaneously explore and synthesize multiple solution paths, combined with novel reinforcement learning algorithms optimized for theorem-proving data.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：reinforcement-learning · model-selection
链接复核时间：2026-09-24
AlphaGeometry neuro-symbolic geometry system
Neuro-symbolic AI system designed to solve complex geometry problems at an Olympiad-level. By pairing a neural language model for intuitive planning (predicting auxiliary points and lines) with a symbolic deduction engine for logical proof construction, it executes rigorous geometric reasoning using a training set of 100 million synthetic geometry proofs generated without human demonstrations.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：agent · code-execution
链接复核时间：2026-09-24
AlphaChip reinforcement learning floorplanning system
Reinforcement learning system designed to optimize computer chip floorplanning by automating the physical placement of blocks (such as memory controllers and processing cores). Treating the layout process as a sequential game to optimize for power, performance, and wire length, it generalizes across architectures to generate optimal physical layouts in hours that traditionally required weeks of human effort, and has been used to design multiple generations of Google TPUs.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：reinforcement-learning · model-selection
链接复核时间：2026-09-24
FunSearch mathematical algorithm discovery tool
Search-based algorithm discovery framework that pairs an LLM with an automated evaluator to generate verified computer programs for complex mathematical and computer science problems. By using an evolutionary feedback loop that progressively refines a pool of functional code, it has discovered new solutions in extremal combinatorics (the cap set problem) and generated optimized heuristics for NP-hard resource allocation (the bin packing problem).
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：neural-network · deployment
链接复核时间：2026-09-24
AlphaDev assembly algorithm discovery system
Reinforcement learning-based system designed to discover optimized computer science algorithms by operating directly at the assembly instruction level. By modeling algorithm construction as a sequential game and evaluating generated assembly instructions for correctness and execution latency, it discovered faster sorting and hashing algorithms that have been integrated into the LLVM C++ standard library.
证据用途：用于理解、实现、选型、可靠性、安全或迁移判断。
使用边界：只直接证明 Google 的实现与声明；预览模型、价格和生命周期使用前应复核
关联概念：reinforcement-learning · model-selection
链接复核时间：2026-09-24