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Showing papers from Qualcomm AI Reserach Show all papers

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NeurIPS 2026Qualcomm Inc, QualcommQualcommQualcomm AI ReserachQualcomm AI ResearchQuantization

Leech Lattice Vector Quantization for Efficient LLM Compression

Leech lattice vector quantization enables efficient LLM compression via structured high-dimensional packing, achieving state-of-the-art post-training quantization without rotation preprocessing.

Tycho F van der Ouderaa, Mart van Baalen, Paul Whatmough, Markus Nagel

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 1/5
86%Must read
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NeurIPS 2026Qualcomm IncQualcomm AI ReserachNVIDIAEfficient attention & state-space models

The Key to Going Linear: Analysis-Driven Transformer Linearization

Analysis-driven transformer linearization isolates state update design to show delta-style networks outperform gated accumulation via key-dependent rank-1 projections, reducing approximation errors with sink tokens and cache routing to match adaptive caching at 32B scale.

Anna Kuzina, Paul Whatmough, Babak Ehteshami Bejnordi

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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14/20 AI panelreviewers recommend it

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AI panel: 14 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 1/5