Technology is the core driver of Ant's future creation.
At the 2025 World AI Conference, Ant Group unveiled an open‑source High‑Order Program framework that blends intelligent models with engineering safeguards to overcome reliability challenges and enable large‑scale, trustworthy AI applications across finance, security, and healthcare.
The CCF‑Ant Research Fund 2025, now open for its first batch, invites global university and institute researchers to apply by August 25 2025 for up to 50 projects spanning data security, hardware‑software co‑design, supercomputing, and artificial intelligence, with detailed topics, eligibility rules, and submission channels provided.
M2-Reasoning-7B, an open‑source 7B multimodal model from Ant Group, combines a high‑quality data pipeline with dynamic multi‑task training and a novel reward function to deliver state‑of‑the‑art performance on both general and spatial reasoning benchmarks, surpassing many larger competitors.
The EXPRESS Workshop at ISSTA 2025, hosted by Ant Group, featured a keynote by Purdue’s Prof. Zhang on an LLM‑driven “Human‑like AI Auditor” called RepoAudit, which demonstrated high‑accuracy automated code review, uncovering dozens of real bugs and hundreds of zero‑day vulnerabilities across major open‑source projects.
The article reviews Yang Xiaofang’s presentation on trustworthy AI, emphasizing the need for systematic support, inclusive design, and participatory governance, and outlines the evolution, capabilities, risks, and multi‑layered solutions for multi‑agent AI systems.
The World Digital Academy unveiled the AI STR series' first global AI Agent Operation Safety Testing Standard, detailing a full‑link risk analysis framework, novel testing methods, and its role in addressing rising safety concerns as AI agents become mainstream in 2025.
The KAG-Thinker model, a collaborative effort by Ant Group, Zhejiang University, and Tongji University, introduces a hierarchical "breadth splitting + depth solving" framework that enhances logical stability, knowledge utilization, and retrieval robustness for complex multi‑hop reasoning tasks across general and specialized domains.
At ACL 2025 in Vienna, a live paper showcase will dissect four cutting‑edge AI studies: the M3GQA benchmark for multi‑entity graph question answering, an unsupervised morphological tree tokenizer, the GradOT method for training‑free, gradient‑preserving offsite LLM tuning, and a large‑language‑model approach to historical analogy.
In a QECon 2025 talk, Ant Group’s AI team detailed a comprehensive multimodal evaluation framework that leverages large‑model metrics, custom pipelines, and benchmark datasets to assess image generation, speech recognition, and video quality, while also contributing to industry standards and academic research.
This article details how Alibaba's Ant Group leverages multimodal large‑language models and multi‑agent architectures to create a low‑code, AI‑driven UI automation testing framework that improves test coverage, reduces manual effort, and scales across diverse mobile mini‑program scenarios.
