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Shen Yan

ArXiv submitter for Seed2.0 Model Card and ByteDance Seed coauthor of Dynamic Linear Attention. LinkedIn identifies him as Research Scientist at ByteDance / Bytedance Seed responsible for multimodal pretraining; personal homepage records Research Scientist at Google DeepMind, Michigan State University PhD, and earlier work with ByteDance AML and Google Research. Homepage links the same X/Twitter handle.

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Jul 03, 2026

Seed2.0 Model Card: Towards Intelligence Frontier for Real World Complexity

Seed2.0 模型卡把 ByteDance Seed 的新一代模型定位为面向真实复杂任务的生产模型族：它以 Doubao / Trae 等产品流量和用户任务为入口，重构了从长尾知识、复杂指令、搜索、视觉、视频、工具调用、GUI agent 到科学研究任务的评测面，并用 Seed2.0 Pro / Lite / Mini 的性能、成本和案例轨迹证明 ByteDance Seed 已经把模型报告从单点能力榜单推进到“产品需求分布、评测系...

2607.00248-seed2-model-card-real-world-complexity BenchmarkMultimodal ModelTool Use
Jun 09, 2026

Dynamic Linear Attention

DLA 认为 long context linear attention 的核心损失来自固定 state merging 策略把信息密度不同的 token 压进同一个 summary state；它用 token level representation drift 动态决定 state 边界，并在固定容量 cache 中合并低信息密度的相邻 state，让 multi state linear attention 同时具备自适应分辨...

2606.10650-dynamic-linear-attention Linear AttentionLong ContextMemory Architecture