Uncovering the ‘Sandwich’ Bottleneck in Residual Quantized Semantic IDs for Generative Search

This study investigates the “sandwich” bottleneck observed in residual‑quantized semantic identifiers (RQ‑SID) used in generative search and recommendation systems, revealing that token concentration in intermediate codebooks caused by path sparsity and long‑tail distributions degrades performance, and proposes two effective mitigation strategies that improve efficiency and generalization in e‑commerce applications.

Generative Searche-commerce recommendationlong-tail distribution

0 likes · 13 min read

Uncovering the ‘Sandwich’ Bottleneck in Residual Quantized Semantic IDs for Generative Search

JD Retail Technology

Apr 27, 2025 · Artificial Intelligence

Addressing the “Sandglass” Bottleneck in Residual Quantization Semantic Identifiers for Generative Search and Recommendation

The paper identifies a “sandglass” bottleneck in Residual Quantization Semantic Identifiers, where middle‑layer tokens dominate, causing sparse paths and long‑tail distributions that hurt e‑commerce search performance, and demonstrates that adaptive pruning of these tokens restores accuracy and efficiency better than removing the layer entirely.

EMNLPGenerative RecommendationSandglass Bottleneck

0 likes · 11 min read

Addressing the “Sandglass” Bottleneck in Residual Quantization Semantic Identifiers for Generative Search and Recommendation

JD Tech Talk

Apr 27, 2025 · Artificial Intelligence

Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval

This paper investigates the "sandglass" phenomenon in residual‑quantized semantic identifiers for generative search and recommendation, analyzes its causes of path sparsity and long‑tail token distribution, and proposes heuristic and adaptive token‑removal methods that substantially improve model performance in e‑commerce scenarios.

Generative RetrievalRecommendation Systemsadaptive token removal

0 likes · 10 min read

Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval

JD Cloud Developers

Apr 27, 2025 · Artificial Intelligence

Overcoming the Hourglass Effect in Residual Quantization for Generative Retrieval

This paper investigates the “hourglass” phenomenon in residual‑quantized semantic identifiers for generative search and recommendation, revealing that token concentration in intermediate codebooks causes path sparsity and long‑tail distributions, and proposes heuristic layer removal and adaptive token‑pruning strategies that markedly improve model performance.

Generative RetrievalToken Pruninghourglass phenomenon

0 likes · 13 min read

Overcoming the Hourglass Effect in Residual Quantization for Generative Retrieval