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

Using Machine Learning to Predict Effective Compression Algorithms for Heterogeneous Datasets

Abstract: Heterogeneous datasets are prevalent in big-data domains. However, compressing such datasets with a single algorithm results in suboptimal compression ratios. This paper investigates how ...
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 ...