Image created with Gemini. Image prompt: A horizontal 1920s Dada Merz collage on aged cardboard, layered with torn Chinese newsprint columns, a faded vermilion export stamp, a bisected shipping manifest with ledger digits, and a small kraft-paper parcel tied with string, with the title ‘Alibaba’ spelled across the top in mismatched cut-out letterpress letterforms glued at slight angles, flat even lighting, visible paper fiber edges and glue stains, muted palette of cream, kraft brown, vermilion red, ink black and slate blue.
Anthropic claims: Alibaba continues to distill Claude on a large scale to train Qwen. Via Bloomberg Anthropic is accusing Alibaba-linked operators of running a massive campaign to illicitly access Claude through nearly 25,000 fraudulent accounts. According to Bloomberg,”
https://x.com/kimmonismus/status/2069879640835961277
Anthropic accuses Alibaba of campaign to extract AI capabilities
https://www.cnbc.com/2026/06/24/anthropic-alibaba-distillation-campaign.html
Anthropic’s letter accusing Alibaba of distillation.”
https://x.com/Discoplomacy/status/2070069250513900005
How can we train small agentic models that are highly capable of terminal use and coding? Announcing OpenThoughts-Agent + OpenThinkerAgent-32B, the strongest Qwen-3 based open-data agentic model: 44.8% avg across 7 agentic benchmarks! (1/n)”
https://x.com/RichardZ412/status/2069827815403557287
Anthropic Accuses Alibaba of ‘Illicitly’ Accessing AI Models – Bloomberg
https://www.bloomberg.com/news/articles/2026-06-24/anthropic-accuses-alibaba-of-illicitly-accessing-its-ai-models
Alibaba’s AI video model rises to No. 2 in global rankings, as OpenAI’s Sora and ByteDance’s Seedance fall away | VentureBeat
https://venturebeat.com/technology/alibabas-ai-video-model-rises-to-no-2-in-global-rankings-as-openais-sora-and-bytedances-seedance-fall-away
It’s kind of crazy how well LiteParse does on markdown document parsing even compared against frontier VLMs – when it doesn’t use VLMs or any AI/OCR models at all. It’s pure code. On ParseBench, it outperforms Qwen 3.5-9B / GLM-OCR. There’s still a gap vs. models like Gemma 4″
https://x.com/jerryjliu0/status/2068005414369906856
We usually think of data curation as a lever for model quality and training efficiency. There’s a third axis people often miss: test-time compute. Data curation can make models far less verbose at the same performance. Our models are 35x more efficient than Qwen. See thread ⬇️”
https://x.com/pratyushmaini/status/2070172084123390109
📣📣 Meet Qwen-AgentWorld — a native language world model that simulates 7 agent environments (MCP, Search, Terminal, SWE, Web, OS, Android) within a single model. Environment modeling is the training objective from day one, not a post-hoc adaptation. 🤔 LLMs are trained to be”
https://x.com/Alibaba_Qwen/status/2069720365442719867
We open-source Qwen-AgentWorld-35B-A3B (MoE, 35B/3B active, 256K context) and AgentWorldBench. Two routes, one roadmap: 🔬 Build the simulator — scalable, controllable, surpassing real environments 🧠 Internalize world modeling — predict before you act Qwen-AgentWorld is our”
https://x.com/Alibaba_Qwen/status/2069720412481888400
[2606.24597] Qwen-AgentWorld: Language World Models for General Agents
https://arxiv.org/abs/2606.24597
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