VQL: An End-to-End Context-Aware Vector Quantization Attention for Ultra-Long User Behavior Modeling

• URL: http://arxiv.org/abs/2508.17125v1
• Date: Sat, 23 Aug 2025 19:58:18 GMT
• Title: VQL: An End-to-End Context-Aware Vector Quantization Attention for Ultra-Long User Behavior Modeling
• Authors: Kaiyuan Li, Yongxiang Tang, Yanhua Cheng, Yong Bai, Yanxiang Zeng, Chao Wang, Xialong Liu, Peng Jiang,
• Abstract summary: In large-scale recommender systems, ultra-long user behavior sequences encode rich signals of evolving interests.<n>We propose VQL, a context-aware Vector Quantization Attention framework for ultra-long behavior modeling.
• Score: 12.619238878583703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In large-scale recommender systems, ultra-long user behavior sequences encode rich signals of evolving interests. Extending sequence length generally improves accuracy, but directly modeling such sequences in production is infeasible due to latency and memory constraints. Existing solutions fall into two categories: (1) top-k retrieval, which truncates the sequence and may discard most attention mass when L >> k; and (2) encoder-based compression, which preserves coverage but often over-compresses and fails to incorporate key context such as temporal gaps or target-aware signals. Neither class achieves a good balance of low-loss compression, context awareness, and efficiency. We propose VQL, a context-aware Vector Quantization Attention framework for ultra-long behavior modeling, with three innovations. (1) Key-only quantization: only attention keys are quantized, while values remain intact; we prove that softmax normalization yields an error bound independent of sequence length, and a codebook loss directly supervises quantization quality. This also enables L-free inference via offline caches. (2) Multi-scale quantization: attention heads are partitioned into groups, each with its own small codebook, which reduces quantization error while keeping cache size fixed. (3) Efficient context injection: static features (e.g., item category, modality) are directly integrated, and relative position is modeled via a separable temporal kernel. All context is injected without enlarging the codebook, so cached representations remain query-independent. Experiments on three large-scale datasets (KuaiRand-1K, KuaiRec, TMALL) show that VQL consistently outperforms strong baselines, achieving higher accuracy while reducing inference latency, establishing a new state of the art in balancing accuracy and efficiency for ultra-long sequence recommendation.

Related papers

• InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models [4.4248984733976275]
  InnerQ is a hardware-aware KV-cache quantization scheme that decodes latency without sacrificing accuracy.<n>It applies group-wise quantization while grouping the cache matrices over their inner dimension.<n>Our evaluation experiments on Llama models shows that InnerQ maintains a few-shot GSM8K performance comparable to non-quantized KV caches.
  arXiv  Detail & Related papers  (2026-02-26T16:50:36Z)
• Scalable Sequential Recommendation under Latency and Memory Constraints [0.14053129774629072]
  Sequential recommender systems must model long-range user behavior while operating under strict memory and latency constraints.<n> Transformer-based approaches achieve strong accuracy but suffer from quadratic attention complexity.<n>This paper presents HoloMambaRec, a lightweight sequential recommendation architecture that combines holographic reduced representations for attribute-aware embedding.
  arXiv  Detail & Related papers  (2026-01-13T09:16:49Z)
• Training-free Context-adaptive Attention for Efficient Long Context Modeling [57.703159205740185]
  Training-free Context-adaptive Attention (TCA-Attention) is a training-free sparse attention mechanism that selectively attends to only the informative tokens for efficient long-context inference.<n>TCA-Attention achieves a 2.8$times$ speedup and reduces KV cache by 61% at 128K context length while maintaining performance comparable to full attention.
  arXiv  Detail & Related papers  (2025-12-10T01:54:57Z)
• OmniSparse: Training-Aware Fine-Grained Sparse Attention for Long-Video MLLMs [43.78743496579736]
  We introduce OmniSparse, a training-aware fine-grained sparse attention framework for long-video MLLMs.<n>Experiment results show that OmniSparse matches the performance of full attention while achieving up to 2.7x speedup during prefill and 2.4x memory reduction during decoding.
  arXiv  Detail & Related papers  (2025-11-15T13:14:17Z)
• VecInfer: Efficient LLM Inference with Low-Bit KV Cache via Outlier-Suppressed Vector Quantization [23.781285860723248]
  Key-Value ( KV) cache introduces memory overhead during large language model (LLM) inference.<n>We propose VecInfer, a novel VQ method for aggressive KV cache compression while enabling efficient inference.<n>VecInfer consistently outperforms existing quantization baselines across both long-context understanding and mathematical reasoning tasks.
  arXiv  Detail & Related papers  (2025-10-07T17:35:28Z)
• QuantSparse: Comprehensively Compressing Video Diffusion Transformer with Model Quantization and Attention Sparsification [67.15451442018258]
  Diffusion transformers exhibit remarkable video generation capability, yet their prohibitive computational and memory costs hinder practical deployment.<n>Model quantization and attention sparsification are two promising directions for compression, but each alone suffers severe performance degradation under aggressive compression.<n>We propose textbfQuantSparse, a unified framework that integrates model quantization with attention sparsification.
  arXiv  Detail & Related papers  (2025-09-28T06:49:44Z)
• MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation [74.34220141721231]
  We present MPQ-DMv2, an improved textbfMixed textbfPrecision textbfQuantization framework for extremely low-bit textbfDiffusion textbfModels.
  arXiv  Detail & Related papers  (2025-07-06T08:16:50Z)
• Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics [64.62231094774211]
  Statefuls (e.g., Adam) maintain auxiliary information even 2x the model size in order to achieve optimal convergence.<n>SOLO enables Adam-styles to maintain quantized states with precision as low as 3 bits, or even 2 bits.<n>SOLO can thus be seamlessly applied to Adam-styles, leading to substantial memory savings with minimal accuracy loss.
  arXiv  Detail & Related papers  (2025-05-01T06:47:45Z)
• LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification [42.54363549922909]
  LongSpec is a framework that addresses the challenges of efficient inference over long contexts.<n>LongSpec achieves up to a 3.26x speedup over strong Flash Attention baselines.<n>The code is available at https://github.com/sail-sg/LongSpec.
  arXiv  Detail & Related papers  (2025-02-24T18:53:31Z)
• LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory [63.41820940103348]
  Self-attention mechanism's computational cost limits its practicality for long sequences. We propose a new method called LongVQ to compress the global abstraction as a length-fixed codebook. LongVQ effectively maintains dynamic global and local patterns, which helps to complement the lack of long-range dependency issues.
  arXiv  Detail & Related papers  (2024-04-17T08:26:34Z)
• SqueezeLLM: Dense-and-Sparse Quantization [80.32162537942138]
  Main bottleneck for generative inference with LLMs is memory bandwidth, rather than compute, for single batch inference. We introduce SqueezeLLM, a post-training quantization framework that enables lossless compression to ultra-low precisions of up to 3-bit. Our framework incorporates two novel ideas: (i) sensitivity-based non-uniform quantization, which searches for the optimal bit precision assignment based on second-order information; and (ii) the Dense-and-Sparse decomposition that stores outliers and sensitive weight values in an efficient sparse format.
  arXiv  Detail & Related papers  (2023-06-13T08:57:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.