Official Amap technology account showcasing all of Amap's technical innovations.
Omni-WorldBench introduces a comprehensive benchmark that shifts world‑model evaluation from visual fidelity to interactive response, detailing its two‑part suite, metric design, extensive prompt taxonomy, and experimental results that reveal current models' strengths and limitations in causal and temporal reasoning.
ABot-M0 is an open‑source Vision‑Language‑Action foundation model that unifies fragmented robot data, introduces Action Manifold Learning for smoother action prediction, and offers a plug‑and‑play dual‑stream perception architecture, achieving state‑of‑the‑art results on major manipulation benchmarks.
ABot-PhysWorld introduces a physically consistent video generation framework for embodied AI, leveraging the PAI‑Bench benchmark, large‑scale multi‑modal data, DPO preference alignment, and dense action maps to surpass SOTA models in both visual quality and physical plausibility across diverse robotic tasks.
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.
