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

Fault Diagnosis of Complex System via Quantification of Complexity With Lossless Compression

Abstract: Data-driven fault diagnosis methods for complex systems, based on machine learning and deep learning, have shown competitive performance. However, these parameterized approaches face two ...

Hidden geometry explains why kernel methods separate complex data so well

Are two sets of data genuinely different, or is it because of randomness? This question, known as the two-sample testing problem, becomes notoriously difficult in modern datasets, because they are ...
IEEE

Next Bit Prediction: A Unified Lossless and Lossy Point Cloud Geometry Compression Framework

Abstract: We propose Next Bit Prediction (NBP), a unified framework that simultaneously addresses lossless compression and lossy reconstruction of 3D point cloud geometry through a next-bit ...