#sparse-attention

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4.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.2 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.8 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election — 1 src1.1 Study Audits Misalignment in Multi-Modal World Models — 1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings — 1 src4.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.2 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.8 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election — 1 src1.1 Study Audits Misalignment in Multi-Modal World Models — 1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings — 1 src

2 curated events

papersTODAY 04:00 UTC

Self-Indexing Attention Unifies Token Retrieval Across Prefill and Decode

A new arXiv paper proposes a self-indexing attention mechanism for sparse long-context LLM inference. The authors note that current sparse methods typically rely on separate retrieval strategies for the prefill and decode stages, which prevents a single retrieval representation from being shared across the whole inference run. Their approach aims to make one retrieval representation reusable throughout, while remaining compatible with compression.

long-context/sparse-attention/inference-efficiency/llm

papersSEP 11 04:00 UTC

HISA: Hierarchical Indexing Method for Fine-Grained Sparse Attention

A new arXiv paper proposes HISA, a hierarchical indexing approach for token-level sparse attention of the kind used in DeepSeek Sparse Attention. Such methods score every past key with a lightweight indexer before running attention on a selected subset, which becomes costly as context grows. HISA aims to make that key-selection step more efficient while keeping fine-grained selection quality.

sparse-attention/efficiency/research/llm