Official Amap technology account showcasing all of Amap's technical innovations.
MobilityBench is an open‑source benchmark built from over 100 000 real user queries that evaluates AI route‑planning agents with a deterministic sandbox, multi‑dimensional metrics, and support for ReAct and Plan‑and‑Execute frameworks, revealing performance gaps between open‑source and closed‑source models.
ABot‑M0 tackles the three long‑standing "Babel Tower" challenges of embodied AI—data fragmentation, inconsistent representations, and training mismatches—by releasing the massive UniACT dataset, introducing Action Manifold Learning for direct action prediction, and designing a plug‑and‑play dual‑path perception architecture that outperforms prior models on multiple robot benchmarks.
The DiffVL framework redefines visual localization as a diffusion‑based GPS denoising task, using BEV‑conditioned visual cues and standard SD maps to achieve sub‑meter accuracy without high‑definition maps, and demonstrates its superiority through extensive autonomous‑driving experiments.
UniMapGen introduces a generative, multimodal framework that models lane lines as token sequences, employs an iterative state‑update mechanism for global consistency, and achieves state‑of‑the‑art performance on large‑scale satellite‑derived map construction, enabling seamless lane‑level navigation worldwide.
After LLMs entered the deep‑water stage, developers realized that agents must go beyond passive Q&A to support asynchronous, long‑running, and subscribable tasks; this article details the design, architecture, and engineering challenges of the “Xiao Gao Teacher AI Agent” smart‑task system, from event‑driven logic to fault‑tolerant deployment.
This article introduces the Agents‑Companion paradigm for Text‑to‑SQL, detailing how self‑describing database agents autonomously mine schema, statistics and semantics to generate high‑quality evidence, thereby bridging the gap between academic research and industrial deployment and significantly improving query accuracy.
Gaode’s technical report details an AI‑driven Interest Route (LOI) system that transforms massive real‑world travel data and user‑generated content into personalized classic and themed itineraries, describing data fusion, query rewriting, multi‑dimensional filtering, schema extraction, scoring, and AIGC content generation.
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.
