Adamas: Hadamard Sparse Attention for Efficient Long-Context Inference

• URL: http://arxiv.org/abs/2510.18413v1
• Date: Tue, 21 Oct 2025 08:44:47 GMT
• Title: Adamas: Hadamard Sparse Attention for Efficient Long-Context Inference
• Authors: Siyuan Yan, Guo-Qing Jiang, Yuchen Zhang, Xiaoxing Ma, Ran Zhu, Chun Cao, Jingwei Xu,
• Abstract summary: We introduce Adamas, a lightweight yet highly accurate sparse attention mechanism designed for long-context inference.<n>Experiments show that Adamas matches the accuracy of full attention with only a 64-token budget, near-lossless performance at 128, and supports up to 8x higher sparsity than prior state-of-the-art (SOTA) methods.
• Score: 15.466168180222164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) now support context windows of hundreds of thousands to millions of tokens, enabling applications such as long-document summarization, large-scale code synthesis, multi-document question answering and persistent multi-turn dialogue. However, such extended contexts exacerbate the quadratic cost of self-attention, leading to severe latency in autoregressive decoding. Existing sparse attention methods alleviate these costs but rely on heuristic patterns that struggle to recall critical key-value (KV) pairs for each query, resulting in accuracy degradation. We introduce Adamas, a lightweight yet highly accurate sparse attention mechanism designed for long-context inference. Adamas applies the Hadamard transform, bucketization and 2-bit compression to produce compact representations, and leverages Manhattan-distance estimation for efficient top-k selections. Experiments show that Adamas matches the accuracy of full attention with only a 64-token budget, achieves near-lossless performance at 128, and supports up to 8x higher sparsity than prior state-of-the-art (SOTA) methods while delivering up to 4.4x self-attention and 1.5x end-to-end speedups on 32K-length sequences. Remarkably, Adamas attains comparable or even lower perplexity than full attention, underscoring its effectiveness in maintaining accuracy under aggressive sparsity.

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