Jul 3, 2026 | The Tongyi Weekly
Your weekly dose of cutting-edge AI from Tongyi Lab – the AI research institute under Alibaba Group
Hello, creators and builders,
This week we’re sharing research that makes long-context models more efficient, a new quantization option from the community for running Qwen3.6-27B on limited hardware, and the first conversation in our Ready to Share series exploring the vision behind LOGOS.
Let’s dive in.
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📚 Research Breakthroughs
HydraHead: From Head-Level Functional Heterogeneity to Specialized Attention Hybridization
We introduced our latest research paper HydraHead, a new attention hybridization architecture that fuses Full Attention and Linear Attention at the head level. Motivated by insights from mechanistic interpretability, HydraHead treats the attention head—not the layer—as the natural granularity for attention hybridization to build more efficient long-context models.
Key Innovations:
An interpretability-driven strategy that preserves Full Attention only for retrieval-critical heads
A scale-normalized fusion module that enables Full and Linear Attention to coexist within the same layer
Trained on only 15B tokens, HydraHead improves long-context performance by 69%+ on NIAH at 512K context, approaching the performance of Qwen3.5.
🔗 Explore more: Paper
✨ Community Spotlights
Efficient Deployment: Qwen3.6-27B-NVFP4 from nvidia
For anyone who wants to run Qwen3.6-27B but doesn’t have the VRAM to spare, NVIDIA just published their official NVFP4 quantized build on Hugging Face.
The major upgrade here is the NVFP4 4-bit float format, which delivers huge VRAM savings by shrinking the model to about a third of the size of the standard BF16 weights.
Community Conversation: Ready to Share Series Ep #01
In the first episode of Ready to Share, we sit down with Zheng Wang, one of the researchers behind LOGOS, to discuss the ideas that inspired the project.
Rather than focusing only on benchmark results, this conversation explores:
The motivation behind representing scientific knowledge through a common language
Technical challenges the team encountered during development
Why scientific domains (proteins, molecules, materials, reactions) could share a unified generative framework
Fresh research, explained by the people who built it.
A great listen for anyone interested in AI for Science.
📬 Want More? Stay Updated.
Every week, we bring you:
New model releases & upgrades
AI research breakthroughs
Open-source tools you can use today
Community highlights that inspire
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Tongyi Lab is a research institution under Alibaba Group dedicated to artificial intelligence and foundation models, focusing on the research, development, and innovative applications of AI models across diverse domains. Its research spans large language models (LLMs), multimodal understanding and generation, visual AIGC, speech technologies, and more.
When Qwen 3.7 27B?