The paper presents a method that uses relation knowledge—either manually crafted or learned from training data—to select a compact set of knowledge‑graph facts for large language models (LLMs). It introduces derivability, consistency, and validation checks to determine whether an answer can be derived from the selected facts, whether the facts conflict, and whether the derived answer should replace the LLM’s output. Experiments show that seven open LLMs perform better with the compact context than with relevance‑ranked facts, achieving higher exact‑match accuracy on ComplexWebQuestions and outperforming SubgraphRAG across 53 LLM, benchmark, and prompt settings.
By Seunghui Jwa, Minsu Oh, Sangwoo Hong, Yeo Chan Yoon
arXiv:2607. 18240v1 Announce Type: new Abstract: Large language models (LLMs) can achieve strong fact-checking accuracy, yet forced binary decisions conceal a critical reliability problem: systems may issue confident verdicts even when supporting evidence is weak, sparse, or internally inconsistent.
By Dekun Yang
arXiv:2608.22762v1 Announce Type: new
Abstract: Knowledge graph question answering (KGQA) is a key task for evaluating KG-augmented Large Language Models (LLMs), and complex KGQA that requires multi-...
By Chenhui Liu, Jianpeng Zhou, Jiahai Wang
The paper introduces Iris-mini and Iris-pro, two search agents trained at 35B and 397B parameter scales. They use a novel data pipeline that constructs reverse‑engineered multi‑hop queries from web hyperlinks, filters trajectories, and alternates supervised fine‑tuning with reinforcement learning in a process called SFT‑RL climbing. Evaluations on several benchmarks show that, with inference‑time context management, the agents achieve the best open‑source results in their parameter ranges.
By Ziyuan Liu, Hengqi Liu, Zichuan Wang, Yang Qin, Jiachen Liang, Xu Chu, Shaowei Chen, Yuantao Gu, Mu Chuan
arXiv:2607. 08269v1 Announce Type: new Abstract: Existing retrieval-augmented generation (RAG) systems treat web pages as flat text, losing the structural and semantic signals encoded in HTML.
By Ying Liu, Yi Ye, Quanyu Feng, Mingxi Ye, Mingtao Zhang, Haoyang Li, Chen Jason Zhang, Qing Li
arXiv:2604. 09251v3 Announce Type: replace Abstract: Deep research agents increasingly interleave web browsing with multi-step computation, yet existing benchmarks evaluate these capabilities in isolation, creating a blind spot in assessing real-world performance.
By Young-Suk Lee, Ramon Fernandez Astudillo, Radu Florian