Large Language Models (LLMs) are limited by their parametric knowledge, leading to hallucinations in knowledge-extensive tasks. To address this, Retrieval-Augmented Generation (RAG) incorporates external document chunks to expand LLM knowledge. Furthermore, compressing information from document chunks through extraction or summarization can improve LLM performance. Nonetheless, LLMs still struggle to notice and utilize scattered key information, a problem known as the "lost-in-the-middle" syndrome. Therefore, we typically need to restructure the content for LLM to recognize the key information. We propose $\textit{Refiner}$, an end-to-end extract-and-restructure paradigm that operates in the post-retrieval process of RAG. $\textit{Refiner}$ leverages a single decoder-only LLM to adaptively extract query-relevant contents verbatim along with the necessary context, and section them based on their interconnectedness, thereby highlights information distinction, and aligns downstream LLMs with the original context effectively. Experiments show that a trained $\textit{Refiner}$ (with 7B parameters) exhibits significant gain to downstream LLM in improving answer accuracy, and outperforms other state-of-the-art advanced RAG and concurrent compressing approaches in various single-hop and multi-hop QA tasks. Notably, $\textit{Refiner}$ achieves a 80.5% tokens reduction and a 1.6-7.0% improvement margin in multi-hop tasks compared to the next best solution. $\textit{Refiner}$ is a plug-and-play solution that can be seamlessly integrated with RAG systems, facilitating its application across diverse open-source frameworks.

The piece seems to conjure a prehistoric avant-garde musical workshop, a sonic analogue of the visual culture that can be glimpsed in the cave. Fully notated passages--scampering runs, precisely hammering chords, ghostly arpeggios--are interspersed with opportunities for improvisation. The first twenty-four bars indicate rhythms, dynamics, and registers but not precise pitches. The ending, too, is left open. Cory Smythe, himself a composer and improviser of note, proved an ideal conduit, making the distinction between Lewis's ideas and his own elaborations inconsequential.

A concert held at the Massachussetts Institute of Technology (MIT) in the fall to celebrate the opening of the university's new museum included a performer that was invisible to the audience but played a key role in forming the melodic sound: an artificial intelligence (AI) system that responded to the musicians and improvised in real time. In a piece from "Brain Opera 2.0," the system starts by growling to the trumpet, then finds pitches with the trombone, becomes melodic with the sax, and ultimately syncs with the instruments by the time everyone comes in, explains Tod Machover, a music and media professor at MIT and head of the MIT Media Lab, who served as composer/conductor of the two-night concert event. The "living, singing AI" system was designed by Manaswi Mishra, one of Machover's Ph.D. students. "We developed a machine learning-based model that could react to musician input in real time, and then'fed' this model with a vast amount of music from many countries, styles, and historic periods, as well as with all kinds of human voices making every conceivable kind of vocal sound," Machover said. The system also drew from a vast library of percussive instruments and sounds from around the world to then improvise with the performers.