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Latest from Alimama Tech

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Alimama Tech

Aug 13, 2025 · Information Security

How Private Set Operations Secure Data Collaboration in the Big Data Era

Private Set Operations (PSO) enable multiple parties to perform set intersections, unions, and related computations on encrypted data, preserving privacy through cryptographic techniques such as public‑key encryption, oblivious transfer, and garbled circuits, and are applied across advertising, finance, healthcare, and government for secure data collaboration.

AdvertisingCryptographyData Security

0 likes · 20 min read

How Private Set Operations Secure Data Collaboration in the Big Data Era

Alimama Tech

Aug 6, 2025 · Information Security

How Privacy-Enhancing Technologies Are Revolutionizing Data Use in Digital Advertising

This article reviews the background, core techniques, and typical applications of privacy‑enhancing technologies—including secure multi‑party computation, privacy‑preserving machine learning, differential privacy, and trusted execution environments—highlighting their role in unlocking multi‑party data value while ensuring compliance and privacy protection.

Privacy Computingdifferential privacyfederated learning

0 likes · 20 min read

How Privacy-Enhancing Technologies Are Revolutionizing Data Use in Digital Advertising

Alimama Tech

Aug 6, 2025 · Artificial Intelligence

How ComRecycle Cuts CPU/GPU Use by 23% in Taobao Ads: An Intelligent Computation Recycling Framework

This paper introduces ComRecycle, an intelligent computation recycling framework for Taobao's display advertising system that caches and reuses ad candidates across recall, coarse‑ranking, and fine‑ranking stages, achieving up to 23% CPU and 22% GPU savings while maintaining recommendation quality.

Online AdvertisingUplift Modelingcomputation recycling

0 likes · 17 min read

How ComRecycle Cuts CPU/GPU Use by 23% in Taobao Ads: An Intelligent Computation Recycling Framework

Alimama Tech

Jul 23, 2025 · Artificial Intelligence

How Differentiable Solver Search Accelerates Diffusion Model Sampling

This article presents a differentiable solver search method that quickly finds high‑quality sampling paths for diffusion models, demonstrating significant FID improvements across Rectified‑Flow, DDPM/VP, and text‑to‑image models while requiring no model parameter changes.

AIdifferentiable solverdiffusion models

0 likes · 20 min read

How Differentiable Solver Search Accelerates Diffusion Model Sampling

Alimama Tech

Jul 17, 2025 · Artificial Intelligence

How to Build a High‑Scoring AI Werewolf Agent: Strategies, Prompt Engineering, and Code

This article details the author's experience designing a top‑performing AI Werewolf agent for the Taotian Group's AI Werewolf Challenge, covering game rules, core challenges, prompt engineering, caching, concurrent requests, model selection, reinforcement‑learning‑style tuning, and tactical strategies for each role, with code examples.

AI AgentLLMPrompt Engineering

0 likes · 25 min read

How to Build a High‑Scoring AI Werewolf Agent: Strategies, Prompt Engineering, and Code

Alimama Tech

Jul 11, 2025 · Artificial Intelligence

Inside Alibaba’s Taotian AI Team: From Werewolf Games to RecGPT

This article explores Alibaba’s Taotian technology team, revealing how its young engineers blend cutting‑edge AI research with playful projects like AI‑driven Werewolf games, develop large‑scale recommendation models such as RecGPT, and foster a culture of curiosity, rapid experimentation, and flat, tech‑first management.

AIGCArtificial IntelligenceR&D management

0 likes · 24 min read

Inside Alibaba’s Taotian AI Team: From Werewolf Games to RecGPT

Alimama Tech

Jul 9, 2025 · Artificial Intelligence

How to Make LLMs Recognize and Resolve Their Own Uncertainty

This article introduces ConfuseBench, a benchmark that classifies LLM uncertainty into document‑missing, ability‑limited, and ambiguous types, and presents methods—including retrieval, chain‑of‑thought, and clarification—to detect and actively resolve uncertainty, improving answer quality across diverse tasks.

Chain-of-ThoughtClarificationInquiry

0 likes · 17 min read

How to Make LLMs Recognize and Resolve Their Own Uncertainty

Alimama Tech

Jun 25, 2025 · Artificial Intelligence

Introducing ROLL: A Scalable, User‑Friendly RL Framework for Large‑Scale LLM Training

ROLL is an open‑source reinforcement‑learning framework designed for large language model post‑training that combines multi‑task RL, agentic support, flexible algorithm configuration, elastic resource scheduling, and rich observability, delivering significant accuracy gains across benchmarks while remaining easy to use for researchers, product developers, and infrastructure engineers.

AI frameworkRLHFScalable Training

0 likes · 11 min read

Introducing ROLL: A Scalable, User‑Friendly RL Framework for Large‑Scale LLM Training

Alimama Tech

May 14, 2025 · Artificial Intelligence

Deep Research‑Driven Risk Root‑Cause Analysis with Domain Graph Constraints for Large‑Scale Advertising Traffic

This article presents a large‑scale advertising risk‑control solution that combines deep‑research paradigms, domain‑graph constraints, and large language models to enable explainable, responsible, and high‑precision fraud detection, detailing system architecture, challenges, demo workflow, and future directions.

AIDeep Researchadvertising fraud

0 likes · 11 min read

Deep Research‑Driven Risk Root‑Cause Analysis with Domain Graph Constraints for Large‑Scale Advertising Traffic

Alimama Tech

May 12, 2025 · Artificial Intelligence

Universal Recommendation Model (URM): A General Large‑Model Recall System for Advertising

The article presents the Universal Recommendation Model (URM), a large‑language‑model‑based recall framework that integrates world knowledge and e‑commerce expertise through knowledge injection and prompt‑driven alignment, achieving significant offline recall gains and a 3.1% increase in ad consumption while meeting high‑QPS, low‑latency production constraints.

AdvertisingPrompt Engineeringhigh QPS

0 likes · 17 min read

Universal Recommendation Model (URM): A General Large‑Model Recall System for Advertising