A product-key parametric memory head with selective sparse updates mitigates catastrophic forgetting in generative retrieval models during sequential addition of new documents.
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7 Pith papers cite this work. Polarity classification is still indexing.
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MV-HNSW is the first native hierarchical graph index for multi-vector data, achieving over 90% recall with up to 14x lower search latency than prior filter-and-refine approaches across seven datasets.
RNSG approximates the range-aware relative neighborhood graph (RRNG) to enable high-performance range-filtered ANN queries with one compact index instead of many.
ComeIR introduces dual-level Engram memory and memory-restoring prediction to reconstruct SID-token embeddings and restore token granularity in generative recommendation.
LSM-VEC integrates hierarchical graphs with LSM-tree levels for out-of-place dynamic updates, sampling-based search, and connectivity-aware reordering, outperforming prior disk-based ANN systems on billion-scale data with higher recall, lower latency, and over 66% memory reduction.
DPPMG learns discrete modal-specific preferences via a dedicated GNN from multimodal user data, quantizes them into tokens, and feeds them into generators with a consistency reward to produce personalized text and images.
citing papers explorer
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A Parametric Memory Head for Continual Generative Retrieval
A product-key parametric memory head with selective sparse updates mitigates catastrophic forgetting in generative retrieval models during sequential addition of new documents.
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Unified and Efficient Approach for Multi-Vector Similarity Search
MV-HNSW is the first native hierarchical graph index for multi-vector data, achieving over 90% recall with up to 14x lower search latency than prior filter-and-refine approaches across seven datasets.
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RNSG: A Range-Aware Graph Index for Efficient Range-Filtered Approximate Nearest Neighbor Search
RNSG approximates the range-aware relative neighborhood graph (RRNG) to enable high-performance range-filtered ANN queries with one compact index instead of many.
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Conditional Memory Enhanced Item Representation for Generative Recommendation
ComeIR introduces dual-level Engram memory and memory-restoring prediction to reconstruct SID-token embeddings and restore token granularity in generative recommendation.
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LSM-VEC: A Large-Scale Disk-Based System for Dynamic Vector Search
LSM-VEC integrates hierarchical graphs with LSM-tree levels for out-of-place dynamic updates, sampling-based search, and connectivity-aware reordering, outperforming prior disk-based ANN systems on billion-scale data with higher recall, lower latency, and over 66% memory reduction.
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Discrete Preference Learning for Personalized Multimodal Generation
DPPMG learns discrete modal-specific preferences via a dedicated GNN from multimodal user data, quantizes them into tokens, and feeds them into generators with a consistency reward to produce personalized text and images.
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