@inproceedings{sood-etal-2026-dropping,
title = "Dropping the Anchor: Statistical Context Summarization for Distributed Systems via Pulsar Attention",
author = "Sood, Aryan and
Acharya, Shantanu and
Nayak, Gaurav Kumar",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1330/",
doi = "10.18653/2026.findings-emnlp.1330",
pages = "28176--28191",
ISBN = "979-8-89176-462-0",
abstract = "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 $3.3\times$ over Star Attention while retaining an identical KV cache footprint. On RULER with Llama-3.1-8B-Instruct, Pulsar Attention outperforms Star Attention at sequence lengths up to 128K tokens and remains competitive with dense attention across most tasks, with task-dependent absolute gains of up to 4.7{\%} over the dense baseline."
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<abstract>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 3.3\times over Star Attention while retaining an identical KV cache footprint. On RULER with Llama-3.1-8B-Instruct, Pulsar Attention outperforms Star Attention at sequence lengths up to 128K tokens and remains competitive with dense attention across most tasks, with task-dependent absolute gains of up to 4.7% over the dense baseline.</abstract>
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%0 Conference Proceedings
%T Dropping the Anchor: Statistical Context Summarization for Distributed Systems via Pulsar Attention
%A Sood, Aryan
%A Acharya, Shantanu
%A Nayak, Gaurav Kumar
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F sood-etal-2026-dropping
%X 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 3.3\times over Star Attention while retaining an identical KV cache footprint. On RULER with Llama-3.1-8B-Instruct, Pulsar Attention outperforms Star Attention at sequence lengths up to 128K tokens and remains competitive with dense attention across most tasks, with task-dependent absolute gains of up to 4.7% over the dense baseline.
%R 10.18653/2026.findings-emnlp.1330
%U https://aclanthology.org/2026.findings-emnlp.1330/
%U https://doi.org/10.18653/2026.findings-emnlp.1330
%P 28176-28191
Markdown (Informal)
[Dropping the Anchor: Statistical Context Summarization for Distributed Systems via Pulsar Attention](https://aclanthology.org/2026.findings-emnlp.1330/) (Sood et al., Findings 2026)
ACL