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EP170: Qwen3.5 Multimodal Agent
Paper Link: https://qwen.ai/blog?id=qwen3.5
Summary:
The paper titled "Qwen3.5: Towards Native Multimodal Agents" introduces the first model in the Qwen3.5 series, Qwen3.5-397B-A17B, which is a native vision-language model designed for high-performance reasoning, coding, and agentic tasks. Built on an innovative hybrid architecture that fuses linear attention (Gated Delta Networks) with a sparse mixture-of-experts (MoE), the model achieves high inference efficiency by activating only 17 billion of its 397 billion total parameters per forward pass.
Key highlights of the model include:
• State-of-the-Art Performance: It matches the performance of the 1T-parameter Qwen3-Max model while offering significantly improved decoding throughput—ranging from 8.6x to 19.0x faster depending on the context length.
• Massive Context and Multimodality: The model supports a 1M context window and can process up to two hours of video, facilitating tasks like reverse-engineering code from gameplay or turning sketches into frontend code.
• Expanded Multilingualism: Support has grown from 119 to 201 languages and dialects, aiming to foster global AI equity.
• Agentic Capabilities: Through extensive scaling of Reinforcement Learning (RL) tasks and environments, the model shows significant gains in general agent capabilities and tool-use efficiency.
The authors conclude that Qwen3.5 serves as a foundation for universal digital agents, with future work focusing on system integration, persistent memory, and autonomous self-improvement.
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By Yun WuPaper Link: https://qwen.ai/blog?id=qwen3.5
Summary:
The paper titled "Qwen3.5: Towards Native Multimodal Agents" introduces the first model in the Qwen3.5 series, Qwen3.5-397B-A17B, which is a native vision-language model designed for high-performance reasoning, coding, and agentic tasks. Built on an innovative hybrid architecture that fuses linear attention (Gated Delta Networks) with a sparse mixture-of-experts (MoE), the model achieves high inference efficiency by activating only 17 billion of its 397 billion total parameters per forward pass.
Key highlights of the model include:
• State-of-the-Art Performance: It matches the performance of the 1T-parameter Qwen3-Max model while offering significantly improved decoding throughput—ranging from 8.6x to 19.0x faster depending on the context length.
• Massive Context and Multimodality: The model supports a 1M context window and can process up to two hours of video, facilitating tasks like reverse-engineering code from gameplay or turning sketches into frontend code.
• Expanded Multilingualism: Support has grown from 119 to 201 languages and dialects, aiming to foster global AI equity.
• Agentic Capabilities: Through extensive scaling of Reinforcement Learning (RL) tasks and environments, the model shows significant gains in general agent capabilities and tool-use efficiency.
The authors conclude that Qwen3.5 serves as a foundation for universal digital agents, with future work focusing on system integration, persistent memory, and autonomous self-improvement.