Why Embedding Models Cannot Scale to All Retrieval Tasks, A Comprehensive Analysis of LLM-based Reranking Methods, and More!
Vol.119 for Aug 25 - Aug 31, 2025
Stay Ahead of the Curve with the Latest Advancements and Discoveries in Information Retrieval.
This week’s newsletter highlights the following research:
Theoretical Limits of Single-Vector Embedding Models in Information Retrieval, from Google DeepMind
Investigating Why Randomly Truncating Text Embeddings Barely Hurts Performance, from Takeshita et al.
Vector Quantization Attention for Ultra-Long User Behavior Modeling in Recommender Systems, from Kuaishou
Conditional Two-Tower Models for Bootstrapping User-to-Item Retrieval Systems, from Pinterest
Computational Scaling Laws for Zero-Shot Information Retrieval with Decoder Models, from Databricks
A Comprehensive Analysis of LLM-based Reranking Methods, from the University of Innsbruck
Lazy Decoder-Only Architecture for Industrial-Scale Generative Recommendation, from Kuaishou
Dynamic Multi-Task Learning for Scalable Recommendation Systems, from Kuaishou
Enabling Compact Language Models for Agentic RAG Through Distillation-Guided Reinforcement Learning, from Kotoge et al.
Combining ID and Content Embeddings Without Architectural Complexity, from Albatross AI
