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Amap Tech

Official Amap technology account showcasing all of Amap's technical innovations.

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Latest from Amap Tech

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Amap Tech
Amap Tech
Dec 11, 2025 · Artificial Intelligence

How ACoder Achieved Up to 24× Faster Multi‑Platform Development with AI

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.

AI codingLarge Language ModelsSoftware Engineering
0 likes · 19 min read
How ACoder Achieved Up to 24× Faster Multi‑Platform Development with AI
Amap Tech
Amap Tech
Dec 3, 2025 · Artificial Intelligence

How Gaode’s G‑Action Uses Generative AI to Predict Users’ Next Move

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.

AIMap Serviceslarge language model
0 likes · 15 min read
How Gaode’s G‑Action Uses Generative AI to Predict Users’ Next Move
Amap Tech
Amap Tech
Nov 19, 2025 · Artificial Intelligence

How Gaode’s Spacetime‑GR Model Boosts POI Recommendation with AI‑Powered SFT and DPO

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.

AI recommendationDPOSFT
0 likes · 12 min read
How Gaode’s Spacetime‑GR Model Boosts POI Recommendation with AI‑Powered SFT and DPO
Amap Tech
Amap Tech
Nov 4, 2025 · Artificial Intelligence

Spacetime‑GR: AI‑Powered Spatiotemporal Model Transforming POI Recommendations

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.

AmapGenerative AIPOI recommendation
0 likes · 14 min read
Spacetime‑GR: AI‑Powered Spatiotemporal Model Transforming POI Recommendations
Amap Tech
Amap Tech
Oct 27, 2025 · Artificial Intelligence

Turning Maps into a Living Map: Amap’s G-Where Generative AI Recommendation

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.

AIMap ServicesSemantic ID
0 likes · 21 min read
Turning Maps into a Living Map: Amap’s G-Where Generative AI Recommendation
Amap Tech
Amap Tech
Oct 21, 2025 · Artificial Intelligence

How Lane‑Level Maps Are Revolutionizing Navigation and Autonomous Driving

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.

Autonomous DrivingRoadGPTdigital maps
0 likes · 11 min read
How Lane‑Level Maps Are Revolutionizing Navigation and Autonomous Driving
Amap Tech
Amap Tech
Oct 17, 2025 · Artificial Intelligence

How Ranking Improves In-Context Example Retrieval: Insights from NeurIPS ’25

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.

In-Context LearningLarge Language ModelsNeurIPS
0 likes · 10 min read
How Ranking Improves In-Context Example Retrieval: Insights from NeurIPS ’25
Amap Tech
Amap Tech
Oct 15, 2025 · Artificial Intelligence

How AI Powers Gaode’s ‘Car Cruise’ for Seamless, Hands‑Free Navigation

Gaode Map’s Car Cruise transforms navigation into an AI‑native, hands‑free experience by automatically detecting routes, providing real‑time voice alerts, lane‑level guidance, and car‑play integration, all driven by a state‑machine architecture that reduces driver distraction while enhancing safety, efficiency, and user personalization.

AI navigationMobile AppState machine
0 likes · 19 min read
How AI Powers Gaode’s ‘Car Cruise’ for Seamless, Hands‑Free Navigation
Amap Tech
Amap Tech
Oct 7, 2025 · Artificial Intelligence

Farsighted-LAM & SSM-VLA: Boosting Spatial‑Temporal Reasoning for Embodied AI

Introducing Farsighted-LAM, a novel latent action model that integrates geometric perception and multi‑scale temporal modeling, and its end‑to‑end SSM‑VLA framework with a Chain‑of‑Thought reasoning module, the authors demonstrate markedly improved spatial‑temporal fidelity, interpretability, and state‑of‑the‑art performance on challenging VLA benchmarks.

chain-of-thoughtembodied AIlatent action models
0 likes · 11 min read
Farsighted-LAM & SSM-VLA: Boosting Spatial‑Temporal Reasoning for Embodied AI
Amap Tech
Amap Tech
Oct 6, 2025 · Artificial Intelligence

Breaking VLA Training Limits: World-Env’s Virtual Sandbox for Safe, Data‑Efficient Robotics

World-Env introduces a virtual training sandbox that eliminates physical interaction, dramatically improves data efficiency with just five expert demos per task, and employs a vision‑language model as a semantic judge to dynamically terminate actions, enabling safe, high‑performing VLA post‑training across diverse robotic benchmarks.

Data EfficiencyVision-Language-Actionvirtual environment
0 likes · 9 min read
Breaking VLA Training Limits: World-Env’s Virtual Sandbox for Safe, Data‑Efficient Robotics