Context compression finally works in production: new research cuts LLM input 16x without the accuracy hit

LCLMs compress LLM context before decode — 8.8x faster at 16x compression, beating every KV cache method tested. Open-sourced by NYU and Columbia.
IEEE

Model Compression Method for S4 With Diagonal State Space Layers Using Balanced Truncation

Abstract: To implement deep learning models on edge devices, model compression methods have been widely recognized as useful. However, it remains unclear which model compression methods are effective ...
IEEE

Robust Blind Watermarking to Secure Color Medical Images Using Multidimensional-FFT Fusing LFSR-Encryption and LZW Compression

Abstract: This paper offers a new robust-blind watermarking scheme for medical image protection. In the digital era, protecting medical images is essential to maintain the confidentiality of patients ...
marktechpost

Meet Turbovec: A Rust Vector Index with Python Bindings, and Built on Google’s TurboQuant Algorithm

Vector search underpins most retrieval-augmented generation (RAG) pipelines. At scale, it gets expensive. Storing 10 million document embeddings in float32 consumes 31 GB of RAM. For dev teams running ...
marktechpost

A Coding Implementation to Compress and Benchmark Instruction-Tuned LLMs with FP8, GPTQ, and SmoothQuant Quantization using llmcompressor

In this tutorial, we explore how to apply post-training quantization to an instruction-tuned language model using llmcompressor. We start with an FP16 baseline and then compare multiple compression ...