Generative Embeddings with Test-Time Scaling, Toward Efficient Visual Document Retrievers, and More!
Vol.124 for Sep 29 - Oct 05, 2025
Stay Ahead of the Curve with the Latest Advancements and Discoveries in Information Retrieval.
This week’s newsletter highlights the following research:
ModernVBERT: Efficient Visual Document Retrieval via Bidirectional Encoders and Late Interaction, from Teiletche et al.
Test-Time Scalable Text Embeddings via Iterative Contrastive Refinement, from National Taiwan University
Understanding Scaling Laws in Generative Recommendation Systems, from Snap Inc.
Fine-tuning with RAG for Improving LLM Learning of New Skills, from Imperial College London
Achieving State-of-the-Art Text Embeddings with 6 Million Open-Source Training Examples, from CodeFuse AI
Multi-Agent Deep Search Over Structured and Unstructured Graph Knowledge, from Yang et al.
Optimizing LLMs for Reasoning-Intensive Document Retrieval Through Rubric-Based Scoring, from Lan et al.
Scalable Deep Research with 4B Parameter Models, from Fractal AI Research
Adaptive Patch-Level Embedding Pruning for Storage-Efficient Visual Document Retrieval, from Yan et al.
Multilingual Learned Sparse Retrieval Through Lexical Space Alignment, from Nguyen et al.
