The paper introduces KBevo, a co‑evolving framework that simultaneously builds a structured knowledge base and performs reasoning over it for knowledge‑intensive question answering. By optimizing both components end‑to‑end with QA outcome rewards, the system improves the quality and connectivity of the knowledge base, leading to higher answer reachability and better compositional factual reasoning. Compared to standard retrieval baselines, KBevo offers greater controllability and improved factual accuracy.
By Ryan Thomas Noonan, Linxi Zhao, Menghan Xu, Akanksha Sarkar, Mihir Mishra, Dongyoung Go, Kilian Q. Weinberger, Yoav Artzi, Jennifer J. Sun
Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty. This wastes computation on easy questions, starves hard ones, and gives no signal for when a generated answer can be trusted.
arXiv:2606. 29090v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty.
By Ansh Kamthan
arXiv:2606. 06197v1 Announce Type: cross Abstract: Question answering (QA) systems have achieved notable progress with the advent of large language models (LLMs).
By Hafez Abdelghaffar, Ahmed Alansary, Ali Hamdi
arXiv:2610.01936v2 Announce Type: replace
Abstract: Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across natural language tasks but remain fundamentally limited by...
By Meghana Sunil, Shravya V, Shravan Venkatraman, Joe Dhanith PR
arXiv:2402. 01767v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) has rapidly advanced the language model field, particularly in question-answering (QA) systems.
By Xinyue Chen, Pengyu Gao, Jiangjiang Song, Xiaoyang Tan