RAG precision tuning can quietly cut retrieval accuracy by 40%, putting agentic pipelines at risk

Fine-tuning RAG embedding models for precision triggers a retrieval accuracy tradeoff that standard benchmarks won't catch ...
i-SCOOP

Inside Google’s TPU 8t and TPU 8i, the silicon built for the agentic era

Google's eighth-generation TPUs split training and inference into two specialised chips. Here's how TPU 8t and TPU 8i work, ...
IEEE

A Comprehensive Analysis of Text Similarity Metrics and Vectorization Techniques for Content-Based Product Recommendation

Abstract: This paper proposes a benchmark analysis of various similarity metrics and text vectorization methods applied to content-based product recommendation systems in e-commerce. It presents an ...
GitHub

ax_python_devel.m4

# Note: Defines as a precious variable "PYTHON_VERSION". Don't override it # in your configure.ac. # This macro checks for Python and tries to get the include path to # 'Python.h'. It provides the ...
GitHub

LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval

LaSER is a novel self-distillation framework designed to bridge the gap between powerful but slow explicit reasoning (like Chain-of-Thought) and efficient but shallow dense retrieval. While Large ...
IEEE

From Weak Textures to Dense Arrangements: Leveraging Prior Knowledge for Small-Object Detection in Remote Sensing Images

Abstract: Detecting small objects in remote sensing images (RSIs) is a significant challenge due to their weak texture, scale variations, and dense spatial arrangements. Existing approaches often ...