Alibaba's Qwen team released Qwen-AgentWorld on Tuesday — two models trained not to act inside agent environments, but to predict what those environments return. The release covers seven domains under a single architecture: MCP, Search, Terminal, Software Engineering, Android, Web, and OS. The release extends Alibaba's recent push into autonomous agents. Qwen3.7-Max, released in May, was built around a 35-hour autonomous execution capability. That shift targets a ceiling teams training agents at scale run into directly. Real search engines surface whatever results exist, with no mechanism to inject controlled conditions. Live terminals do not allow injecting a low-disk-space condition on demand. Agent training is bounded by what production environments will surface, with no sys [...]
Alibaba Cloud on Sunday released HappyHorse 1.1, a major upgrade to its AI video generation model that the company says delivers production-ready video synthesis across core content creation scenarios [...]
Alibaba this week released Qwen3.7-Plus, the latest AI large language model (LLM) in its globally beloved and increasingly expansive Qwen family, boasting more multimodal capabilities and a 60% lower [...]
A rogue AI agent at Meta passed every identity check and still exposed sensitive data to unauthorized employees in March. Two weeks later, Mercor, a $10 billion AI startup, confirmed a supply-chain br [...]
The AI industry has fully entered the "agent era," a paradigm where AI models do far more than generate text — they now actively plan, execute, and course-correct complex tasks over days r [...]
Alibaba dropped Qwen3.5 earlier this week, timed to coincide with the Lunar New Year, and the headline numbers alone are enough to make enterprise AI buyers stop and pay attention.The new flagship ope [...]
On Sunday, a team of nine researchers at Sina Weibo — the Chinese social media giant better known for its microblogging platform than for cutting-edge artificial intelligence — quietly posted a 14 [...]
New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI [...]
AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology [...]