Official Amap technology account showcasing all of Amap's technical innovations.
Gaode Maps and Tongyi DeepResearch unveil ArenaRL, an open‑domain reinforcement‑learning framework that replaces absolute scoring with relative ranking, uses self‑play and a linear‑complexity tournament, and demonstrates measurable gains on POI ranking and complex travel‑planning tasks.
This article details the AI techniques behind Gaode's "Street Ranking" project, explaining the Fancy video concept, the dual training and production pipelines, and the use of SFT, reinforcement learning, MoE‑LoRA, distribution‑matching distillation, and quality‑filtering to achieve 25× faster generation with high aesthetic fidelity.
This article details how Gaode's G‑Plan combines large‑model AI agents, generative ranking, and spatiotemporal counterfactual DPO to model and prioritize multiple user intents on the home page, presents the system architecture, experimental setup, online gains, and ablation results, and explains how it moves recommendation from passive to proactive planning.
The ACoder platform combines multi‑model AI, a panoramic code‑understanding engine, and a layered knowledge‑management system to automate the entire software‑development lifecycle, delivering 5‑20× overall efficiency gains, up to 24× speed‑up for cross‑platform code migration, and dramatically higher code‑recall accuracy.
Gaode’s G‑Action framework combines large‑language‑model pre‑training with fine‑tuned generative recommendation to predict a user’s immediate action and destination, transforming static map services into a dynamic, context‑aware experience and delivering measurable gains in click‑through and engagement metrics.
Gaode transforms its map app into a dynamic, AI‑driven “living map” by fine‑tuning the large Spacetime‑GR model through embedding‑based and generative ranking SFT, DPO alignment, and multimodal augmentation, achieving significant offline CTR‑AUC improvements and online CTR gains in POI recommendation.
This article introduces Spacetime‑GR, a large‑scale generative recommendation model that integrates hierarchical geographic POI indexing and spatiotemporal token encoding to enhance POI prediction for Amap, detailing its pre‑training pipeline, data cleaning, curriculum learning strategy, experimental results, scaling law observations, and the resulting improvements in hit rate and discovery rate.
Amap upgrades its homepage recommendation by integrating large‑model capabilities—G‑Where, G‑Action, and G‑Plan—through semantic ID generation, item tokenization, and multi‑stage LLM training, achieving significant offline and online performance gains while illustrating a scalable generative recommendation framework.
Gaode’s lane‑level map technology evolves from LiDAR‑based methods to multimodal large‑model generation, delivering meter‑level navigation, empowering advanced driver‑assistance and autonomous driving, while its RoadGPT model and recent research breakthroughs illustrate a new era of dynamic, intelligent spatial intelligence.
This article explains the limitations of current pointwise in‑context learning methods, introduces a novel ranking‑based approach called SeDPO that learns preference orders among examples, and demonstrates its superior performance across multiple NLP tasks through extensive experiments and ablation studies.
