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Naive-N0.5-Flash is an open-weight **309B MoE model with 15.5B active parameters**, built for **coding and AI R&D**. It supports a **native 1M-token context window** through a hybrid of Sliding-Window Attention (SWA) and lightweight DeepSeek Sparse Attention (DSA), with no full-attention layers. Naive-N0.5-Flash builds on the open-weight MiMo-V2.5 base model, which has a simple architecture with strong foundational capabilities in world knowledge and deep research. Most layers use Sliding-Window Attention (SWA), whose per-token decoding cost does not grow with context length, while a small number of global-attention layers preserve long-range information. At million-token context lengths, however, these global-attention layers account for much of the decoding overhead. Naive-N0.5-Flash replaces the global-attention layers with DeepSeek Sparse Attention (DSA). A lightweight indexer scores the full history, while the backbone computes attention only over a selected subset of tokens. Although the indexer still scans the full history and the full KV cache is retained, sparse attention substantially reduces attention computation and memory access. Adapting the model to this new attention structure was one objective of continued pretraining.

Text generation
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