Dropping the Anchor: Statistical Context Summarization for Distributed Systems via Pulsar Attention
Inference with large language models (LLMs) on long sequences is computationally expensive due to the quadratic complexity of self-attention. Distributed blockwise methods such as Star Attention reduce this cost by sharding context across hosts, but rely on prepending a static, content-blind copy of the first block to every host. We propose Pulsar Attention, which replaces the static anchor with two lightweight, content-aware components: a small attention-sink prefix that stabilizes softmax, and compact cross-block summaries built via a Max-IDF heuristic that selects chunks containing globally rare tokens. This reduces the Phase 1 per-GPU FLOPs by up to 3.3x over Star Attention while reta...
| Authors | Aryan Sood, Shantanu Acharya, Gaurav Kumar Nayak |
|---|---|
| Primary category | cs.CL |
| Published | 14 May 2026 |
| Last updated at source | 7 August 2026 |
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