The paper evaluates how modern large language models use internal web search to answer factual questions. Using 783 static queries and 288 dynamic queries, the authors find that enabling retrieval improves accuracy on static questions but hurts confidence calibration. On dynamic queries, models often retrieve but still achieve less than 70% accuracy, mainly due to poor query formulation and source selection, indicating that internal web search works better as a quick verification tool than a full information‑retrieval system.
By Sahil Kale
arXiv:2606. 18001v1 Announce Type: new Abstract: Knowledge graph (KG) foundation models (KGFMs) are zero-shot generalizers: trained once, they can predict links on unseen graphs without retraining.
By Cosimo Gregucci, Obaidah Theeb, Daniel Hernandez, Antonio Vergari, Steffen Staab
TailSpec-EASE is a lightweight linear recommender that incorporates a relation‑aware spectral knowledge‑graph prior into a local closed‑form reconstruction objective. By adapting the prior strength to item popularity, it provides stronger semantic guidance for long‑tail items. Across four public benchmarks, it achieves a favorable balance of overall accuracy, long‑tail performance, and training cost, improving NDCG@20 by up to 24% over a no‑KG baseline and training in just 37 seconds on CPU compared to thousands of seconds for GPU‑based KGAT and CPU LightGCN.
By Jianru Shen
arXiv:2607. 28680v1 Announce Type: cross Abstract: Entity linking in tables matches short and ambiguous cell mentions to their corresponding knowledge-base entities.
By Yixin Peng, Kehao Li, Stefan Decker
The paper investigates a parametric approach to knowledge graph memory by compiling each entity into a LoRA adapter, enabling zero‑cost query-time retrieval via weight injection. On the MetaQA dataset, these adapters encode context‑free factual knowledge, improving exact‑match scores by up to +0.243 over a base model and achieving an oracle gap of +0.283. However, the stored knowledge is not recoverable through similarity or embedding‑based methods, indicating that knowledge is stored locally and does not transfer across semantically neighboring entities.
By Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov, Volker Tresp
The paper introduces Knowledge-Weighted Fine‑Tuning, a method that estimates an instance‑level knowledge score through multi‑sampled inference and uses it to scale the learning signal. This approach encourages large language models to explicitly say "I don't know" on out‑of‑scope queries while preserving accuracy on known questions. The authors also propose new evaluation metrics for uncertainty, demonstrating that better discrimination between known and unknown instances improves overall performance.
By Joosung Lee, Hwiyeol Jo, Donghyeon Ko, Kyubyung Chae, Cheonbok Park, Jeonghoon Kim