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SeqFold compresses user states from sequential models, reducing storage by 3,400x

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SeqFold, a novel interface designed to compress and manage user states from frozen sequential models. This system utilizes learned multi-channel fusion, temporal decay, and Lookup-Free Quantization to create compact, timestamp-addressable discrete codes from these states. SeqFold significantly reduces storage requirements, achieving a 3,400x reduction while maintaining downstream ranking performance, making it effective for reusing long-history sequence backbones in various applications. AI

IMPACT Enables more efficient storage and reuse of complex sequential model states, potentially accelerating downstream applications.

RANK_REASON Academic paper detailing a new method for compressing sequential model states. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SeqFold compresses user states from sequential models, reducing storage by 3,400x

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Academic paper detailing a new method for compressing sequential model states. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mianwei Zhou, Duo Zhang, Henry Tsai, Yuqi Zheng, Jian Li, Juexin Lin, Sujay Khandagale, Vijay Rajaram, Dong Jiao, Tao Cheng ·

    SeqFold: Timestamp-Addressable Compressed States for User Action Sequence Backbones

    arXiv:2611.13704v1 Announce Type: new Abstract: Existing sequential user modeling methods mainly focus on improving the backbone encoder or learning task-specific user representations for a single prediction objective. We instead study how to export the multi-layer user states of…

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