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Latest from JD Retail Technology

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JD Retail Technology

Jun 15, 2026 · Artificial Intelligence

Sparse‑Up: Sparse‑Voxel 3D Geometry Generation Boosts Product Reconstruction

The paper presents Sparse‑Up, a sparse‑voxel based 3D geometry generation framework that introduces learnable surface‑anchored upsampling and view‑partition rendering to eliminate redundant points, dramatically reduce memory usage, and achieve high‑fidelity product reconstruction, with experimental results surpassing TRELLIS, Hunyuan3D and other SOTA methods.

3D Geometry GenerationAI3DICASSP 2026

0 likes · 13 min read

Sparse‑Up: Sparse‑Voxel 3D Geometry Generation Boosts Product Reconstruction

JD Retail Technology

Jun 8, 2026 · Mobile Development

Accelerating Taro Native Static Layout Rendering on HarmonyOS

The article analyzes severe scroll jank on low‑end HarmonyOS devices caused by Taro Native's heavyweight card page, identifies main‑thread overload in layout phases 1, 4 and 5, proposes static node‑tree layout with custom measurement interception and font‑measurement caching, and reports a frame‑rate boost from 43 fps to 57 fps (~32.5% improvement).

CustomNodeHarmonyOSNODE_LAYOUT_RECT

0 likes · 8 min read

Accelerating Taro Native Static Layout Rendering on HarmonyOS

JD Retail Technology

Jun 2, 2026 · Artificial Intelligence

RTPrune: Two‑Stage Reading‑Inspired Token Pruning for Efficient DeepSeek‑OCR Inference

The paper presents RTPrune, a token‑pruning technique for DeepSeek‑OCR that exploits a two‑stage reading behavior in LLM decoding, first keeping high‑norm visual tokens and then fusing the rest via optimal‑transport matching with a dynamic pruning‑rate strategy, achieving up to 15% GFLOPs reduction and 18.9% speedup while preserving over 99% OCR accuracy across multiple benchmarks.

DeepSeek-OCROCR efficiencydynamic pruning

0 likes · 9 min read

RTPrune: Two‑Stage Reading‑Inspired Token Pruning for Efficient DeepSeek‑OCR Inference

JD Retail Technology

May 25, 2026 · Artificial Intelligence

How Adaptive Semantic IDs Enable Precise and Generalizable Generative Retrieval

The article introduces the SA²CRQ framework, which adaptively allocates semantic ID length and transfers residual knowledge to resolve head‑item ID collisions and tail‑item generalization gaps in large‑scale e‑commerce generative retrieval, achieving stable gains on both industrial and public datasets.

Adaptive QuantizationEmbeddingLong-tail Distribution

0 likes · 19 min read

How Adaptive Semantic IDs Enable Precise and Generalizable Generative Retrieval

JD Retail Technology

May 19, 2026 · Artificial Intelligence

Spectral Disentanglement and Enhancement: Teaching Multimodal Models to Denoise and Purify

The paper introduces the Spectral Disentanglement and Enhancement (SDE) framework, which uses singular value decomposition to separate strong semantic signals, weak auxiliary signals, and noise, applies curriculum‑based spectral enhancement, and jointly optimizes a dual‑domain contrastive loss, achieving markedly improved robustness and generalization on large‑scale multimodal benchmarks.

Multimodal Learningcontrastive learningdual-domain loss

0 likes · 14 min read

Spectral Disentanglement and Enhancement: Teaching Multimodal Models to Denoise and Purify

JD Retail Technology

Apr 28, 2026 · Mobile Development

How Taro 5.0 Delivers a High‑Performance iOS Rendering Layer and a Unified Cross‑Platform UI Framework

The article explains why existing cross‑platform solutions cannot meet Taro’s demands for high performance, stability, full Taro ecosystem compatibility and low integration cost, and describes the design of a dual‑thread iOS rendering layer, a mixed native‑view + layer approach, and the C++‑based TaroUI framework that provides consistent, high‑speed UI components, a rich‑text engine, virtual list widgets and an extensible architecture for future platforms.

C++ frameworkCross‑Platform UITaRO

0 likes · 19 min read

How Taro 5.0 Delivers a High‑Performance iOS Rendering Layer and a Unified Cross‑Platform UI Framework

JD Retail Technology

Mar 25, 2026 · Databases

How JD.com Scaled POP Order Elasticsearch to Handle Billions of Orders

This article analyzes the challenges of JD.com's POP order Elasticsearch storage—including data skew, oversized shards, frequent updates, and high maintenance costs—and details the multi‑layered architectural redesign that introduced tenant isolation, dual‑hash routing, differentiated shard strategies, and a dual‑active physical foundation to achieve high performance, scalability, and availability.

Data PartitioningElasticSearchorder management

0 likes · 16 min read

How JD.com Scaled POP Order Elasticsearch to Handle Billions of Orders

JD Retail Technology

Mar 3, 2026 · Frontend Development

How JD’s Order Module Achieved One‑Code‑Three‑Platform Success with Taro

This article details JD.com’s six‑month engineering effort to refactor its high‑traffic order module into a single Taro codebase that runs on Android, iOS and HarmonyOS, covering business background, preparation, multi‑mode adaptation, core challenges, quality assurance, efficiency gains and the resulting business impact.

HarmonyOSOrder ModuleTaRO

0 likes · 21 min read

How JD’s Order Module Achieved One‑Code‑Three‑Platform Success with Taro

JD Retail Technology

Jan 30, 2026 · Artificial Intelligence

How JD’s 9N‑LLM Engine Powers Scalable Generative Recommendation at Industrial Scale

The article details JD Retail’s 9N‑LLM unified training engine—supporting TensorFlow and PyTorch, GPU and NPU, and both traditional and generative recommendation scenarios—explaining its architecture, high‑throughput sample engine, distributed sparse embedding system, five‑stage pipeline, UniAttention accelerator, and reinforcement‑learning capabilities that together enable TB‑scale data, B‑scale dense parameters, and efficient RL training for real‑world recommendation services.

Distributed TrainingGPU/NPUUniAttention

0 likes · 26 min read

How JD’s 9N‑LLM Engine Powers Scalable Generative Recommendation at Industrial Scale

JD Retail Technology

Jan 22, 2026 · Operations

How JD Global Sales Boosted UI Test Speed by 100× with a Three‑Layer Multilingual Testing Framework

This article outlines JD Global Sales' multilingual testing challenges, the three‑layer interface‑page‑user‑flow testing architecture, automation and AI‑driven strategies that delivered over 100× UI efficiency and 70%+ API gains while paving the way for continuous globalized quality assurance.

AutomationE-commerceGlobalization

0 likes · 13 min read

How JD Global Sales Boosted UI Test Speed by 100× with a Three‑Layer Multilingual Testing Framework