Technology is the core driver of Ant's future creation.
The EXPRESS 2025 workshop at ISSTA in Norway will showcase AI‑driven code auditing, present cutting‑edge research on trustworthy software systems, and invite researchers and practitioners to discuss transparency, reliability, and security challenges in modern software engineering.
Ring-lite, an open‑source lightweight Mixture‑of‑Experts inference model built on Ling‑lite‑1.5, introduces the C3PO reinforcement‑learning training method and achieves state‑of‑the‑art results on benchmarks such as AIME24/25, LiveCodeBench, CodeForce, and GPQA‑diamond, while offering full transparency of weights, code, and data.
In a detailed AICon talk, Ant Group’s Baoling team leader Zhou Jun outlines their latest large‑model training techniques, MoE architecture optimizations, multimodal breakthroughs, open‑source releases, and the strategic roadmap needed to turn AI into a ubiquitous, “scan‑code‑level” everyday assistant.
A comprehensive study reveals that Model Context Protocol (MCP) platforms lack strict vetting, users struggle to detect malicious servers, and current large language models cannot effectively resist MCP‑level injection attacks, highlighting critical security challenges and proposing mitigation strategies.
The Interactive Intelligence Lab of Ant Technology Research Institute presented 21 accepted CVPR 2025 papers covering visual generation, editing, 3D vision, digital humans and multimodal AI, highlighting tools such as MagicQuill, Lumos, Aurora, FLARE, LeviTor, MangaNinja, AniDoc, Mimir, AvatarArtist, DiffListener, MotionStone, TensorialGaussianAvatars, DualTalk, CompreCap and Uni-AD.
The award‑winning ICSE2025 paper introduces CDFuzz, a lightweight custom dictionary technique that dramatically improves coverage and vulnerability discovery in grey‑box fuzzing, and a live session will dissect its design, validation, and impact on software security testing.
Ant Group’s “Aspect‑Fusion Intelligence” system, recognized as an outstanding cybersecurity case at the 2025 Beijing Cybersecurity Conference, leverages large‑model AI and expert knowledge to enhance threat detection, lower false positives, and improve explainability in large‑scale intrusion detection.
This article presents the LLaDA series of diffusion‑based large language models, explains how their generative‑modeling principle yields language intelligence comparable to autoregressive models, and details the multimodal LLaDA‑V architecture, training methods, experimental results, and broader implications for AI research.
The article explores how DeepSeek R1 and long‑thinking chains have revived interest in machine reasoning, tracing the evolution of natural‑language models, defining reasoning as logical knowledge composition, and outlining future research directions in efficient reasoning architectures and deep‑thinking applications.
The Ant Group’s 10th Technical Open Day gathered leading AI experts who examined the current state and future directions of multimodal large models, embodied AI, world models, transformer architectures, and vertical applications, offering a comprehensive view of the challenges and opportunities on the path toward AGI.
