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Latest from Alimama Tech

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Alimama Tech

Mar 14, 2025 · Artificial Intelligence

Advances in Search Advertising Models with Large Language Models (2024)

In 2024 Alibaba Mama outlines how large‑language models transform search advertising through a three‑line scaling roadmap—explicit inductive‑bias design, implicit compute growth, and auxiliary CV/NLP advances—implemented via a pre‑train/post‑train/CTR paradigm and the LUM user‑behavior model, promising gains in relevance, recall, and real‑time serving while highlighting inference efficiency challenges.

CTR predictionLarge Language Modelsmultimodal embedding

0 likes · 25 min read

Advances in Search Advertising Models with Large Language Models (2024)

Alimama Tech

Mar 12, 2025 · Big Data

Design and Evolution of Alibaba Advertising Real-Time Data Warehouse

Alibaba Mama’s advertising platform migrated from a monolithic Flink‑Kafka pipeline to a layered Paimon lakehouse, adding DWS upsert support and multi‑layer storage, which delivers minute‑level data freshness, cuts latency by 2.5 hours, reduces resource use over 40 %, halves development effort and achieves ≥99.9 % availability.

AdvertisingAlibabaData Lake

0 likes · 18 min read

Design and Evolution of Alibaba Advertising Real-Time Data Warehouse

Alimama Tech

Feb 21, 2025 · Industry Insights

How Paimon + Dolphin Transform Alibaba’s Brand Data Warehouse for Real‑Time Insights

This article analyzes the challenges of Alibaba Mama's brand advertising data warehouse built on a Lambda architecture, introduces Apache Paimon lake storage and Dolphin OLAP engine as a unified lakehouse solution, details implementation steps, performance gains, and business benefits across multiple advertising scenarios.

Big DataDolphinOLAP

0 likes · 15 min read

Alimama Tech

Feb 12, 2025 · Artificial Intelligence

HighService: A High‑Performance Pythonic AI Service Framework for Model Inference and Global Resource Scheduling

HighService, Alibaba’s Pythonic AI service framework, accelerates large‑model inference and maximizes GPU utilization by separating CPU‑GPU processes, offering out‑of‑the‑box quantization, parallelism and caching, and dynamically reallocating idle GPUs across clusters through a master‑worker scheduler to keep online latency low while boosting offline throughput for diffusion and LLM workloads.

AI ServiceDistributed SystemsPython

0 likes · 16 min read

HighService: A High‑Performance Pythonic AI Service Framework for Model Inference and Global Resource Scheduling

Alimama Tech

Jan 8, 2025 · Artificial Intelligence

Model-Based Reinforcement Learning Auto‑Bidding Algorithms for Online Advertising

The paper introduces a model‑based reinforcement‑learning auto‑bidding framework that learns a neural‑network environment model from real logs, generates confidence‑aware virtual data fused with real data, and employs the COMBO+MICRO stabilizer and a Lagrange‑dual method for ROI‑constrained bidding, delivering up to 6.8 % higher consumption, 5 % GMV growth and 3.7 % ROI improvement on Alibaba’s platform.

Online Advertisingauto-biddingbudget constrained bidding

0 likes · 22 min read

Model-Based Reinforcement Learning Auto‑Bidding Algorithms for Online Advertising

Alimama Tech

Dec 25, 2024 · Artificial Intelligence

WiS Platform: Evaluating LLM Multi-Agent Systems via Game-Based Analysis

The WiS Platform provides a game‑based environment for benchmarking large language models in multi‑agent settings, measuring reasoning, deception and collaboration through dynamic scenarios, offering fair experimental design, real‑time competition, visualizations, detailed metrics, and open‑source tools, with GPT‑4o outperforming other models such as Qwen2.5‑72B‑Instruct.

AI evaluationGame-Based TestingLLM research

0 likes · 8 min read

WiS Platform: Evaluating LLM Multi-Agent Systems via Game-Based Analysis

Alimama Tech

Dec 25, 2024 · Artificial Intelligence

Contextual Generative Auction with Permutation-level Externalities for Online Advertising

The paper introduces Contextual Generative Auction (CGA), a generative framework that directly optimizes ad placements while modeling permutation‑level externalities, decouples allocation from payment learning, and achieves near‑optimal Myerson‑style outcomes, delivering up to 3.2% higher RPM, 1.4% more CTR, 6.4% GMV growth, and 3.5% increased advertiser ROI in large‑scale Taobao experiments.

ExternalitiesGenerative ModelsOnline Advertising

0 likes · 18 min read

Contextual Generative Auction with Permutation-level Externalities for Online Advertising

Alimama Tech

Dec 17, 2024 · Artificial Intelligence

Auto‑Bidding in Large‑Scale Auctions: The AIGB Paradigm and the AuctionNet Benchmark

At NeurIPS 2024, Alibaba's AliMama workshop introduced AIGB, a generative-model based auto-bidding solution, and released AuctionNet benchmark with billions of records; the competition attracted 1,861 participants across generative and uncertainty tracks, with winners earning prizes and internships, highlighting broader applications beyond advertising.

AuctionNetNeurIPSdecision intelligence

0 likes · 12 min read

Auto‑Bidding in Large‑Scale Auctions: The AIGB Paradigm and the AuctionNet Benchmark

Alimama Tech

Dec 17, 2024 · Artificial Intelligence

AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games

AuctionNet is a newly introduced benchmark that recreates a massive, realistic online advertising auction environment using latent diffusion‑generated traffic data, provides an 80 GB dataset of 5 × 10⁸ logs from 48 bidding agents, and offers baseline evaluations—including an Online LP that outperforms others—supporting thousands of fair NeurIPS 2024 competition submissions and open‑source tools for large‑scale game decision‑making research.

Generative ModelsOnline Advertisingauto-bidding

0 likes · 15 min read

AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games

Alimama Tech

Dec 11, 2024 · Artificial Intelligence

Engineering Architecture of Alibaba's AI Digital Employee "AI XiaoWan"

Alibaba’s AI digital employee “AI XiaoWan” uses a native multi‑agent architecture where a Controller Agent interprets intent, plans tasks, and orchestrates execution while an Executable Agent performs domain‑specific operations, communicating via a standardized Agent Communication Protocol, leveraging a centralized Tool Center, a retrieval‑augmented knowledge base, and a data‑flywheel feedback loop to continuously improve and evolve toward memory‑based reasoning and self‑learning.

AILarge Language ModelRAG

0 likes · 14 min read

Engineering Architecture of Alibaba's AI Digital Employee "AI XiaoWan"