The paper introduces a three‑stage training pipeline that builds compact, efficient dense retrievers without requiring ground‑truth relevance labels. Using cross‑lingual alignment, relational knowledge distillation, and contrastive fine‑tuning, the authors develop PolDense (six Polish models ranging from 17 M to 1 B parameters) and EuroDense (a 435 M‑parameter model covering nine European languages). Extensive evaluation on 41 Polish and 150 multilingual tasks shows that PolDense‑1B outperforms larger retrievers up to 9 B parameters, while EuroDense leads in task‑averaged and language‑averaged performance among models below 1 B parameters.
By S{\l}awomir Dadas, Rafa{\l} Po\'swiata, Ma{\l}gorzata Gr\k{e}bowiec, Micha{\l} Pere{\l}kiewicz
arXiv:2607. 26070v1 Announce Type: cross Abstract: Large language model (LLM)-based agentic search systems are often evaluated as if the underlying LLM were the only component that matters, yet their measured performance also depends on the surrounding search environment: the Wikipedia snapshot, preprocessing pipeline, chunking policy, retrieval backend, tool schema, observation format, and answer submission rule.
By Guanming Xiong, Penghui Zhang
arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.
By Christopher J. Wedge, Joshua Stutter, Danny Dixon, Jacek Ca{\l}a
The paper introduces a new perspective on entity rarity in multimodal entity linking by using knowledge‑graph structural metrics instead of popularity metrics, revealing many rare entities previously overlooked. Experiments show that state‑of‑the‑art models suffer a 15.4–39.9% accuracy drop on these rare‑entity slices. The authors propose a training‑free framework that combines reasoning and retrieval with a vision‑language model, achieving a 6.9% overall accuracy gain and up to 23.3% improvement on rare entities, and release a new benchmark MERLIN‑Rare for focused evaluation.
By Parinthapat Pengpun, Simran Khanuja, Graham Neubig
arXiv:2607. 21274v1 Announce Type: cross Abstract: We present CUP, a Greek book retrieval benchmark consisting of 868 catalog records and 104 expert-annotated queries with graded relevance judgments.
By Katerina Papantoniou, Panagiotis Papadakos, Theodore Patkos, Dimitris Garefalakis, Nikos Vardakis, Dimitris Plexousakis
The paper introduces TriviaRoomQA, a multilingual benchmark of 3,300 parallel multiple-choice questions across 288 topics, plus an additional 5,340 French-only questions. It evaluates 30 open-weight large language models (7–70 B parameters) from various regions, finding strong performance on knowledge-intensive subjects like history and geography but weaker results on everyday popular‑culture topics such as celebrities, music, movies, and news. The study also shows that model performance varies across languages for the same questions, indicating that factual knowledge access is not always language‑independent.
By Anna Mosolova, Djam\'e Seddah
RIT‑RAG is a retrieval‑augmented generation framework that combines content retrieval with structural navigation of documents. It builds a tree for each document from its table of contents or sitemap, then uses retrieved chunk positions to induce manageable sub‑trees that an LLM agent can navigate, read selectively, and reformulate queries. Experiments on financial, scientific, and customer‑support benchmarks show RIT‑RAG outperforms vanilla, graph‑based, and agentic baselines, achieving up to 11.4‑point accuracy gains on the new EntQABench of 2.84 million technical‑documentation webpages.
By Meghanadh Pulivarthi, Swaraj Kumar Biswal, Kushagra Bhushan, Yatin Nandwani, Sachindra Joshi, Dinesh Raghu
arXiv:2607. 23507v1 Announce Type: cross Abstract: Choosing the right text embedding model is one of the most consequential -- and most frequently under-examined -- decisions in building a retrieval or search system, yet the model that tops a leaderboard is rarely the best choice for a given deployment.
By Madhav S Baidya
arXiv:2606. 13647v1 Announce Type: cross Abstract: We introduce SkMTEB, the first comprehensive MTEB-style text embedding benchmark for Slovak, a low-resource West Slavic language, comprising 31 datasets across 7 task types -- nearly 4$\times$ the depth of existing multilingual benchmark coverage for Slovak.
By Marek \v{S}uppa, Andrej Ridzik, Daniel Hl\'adek, Nat\'alia K\v{n}a\v{z}ekov\'a, Vikt\'oria Ondrejov\'a
arXiv:2608.29953v1 Announce Type: new
Abstract: Flat retrieval-augmented generation treats a corpus as a bag of chunks, discarding document hierarchy and cross document structure. We introduce Search...
By Guransh Singh, Vishwajeet Kumar, Arkadeep Acharya, Adnan Qidwai, Jaydeep Sen, Sachindra Joshi
Quiz rooms, trivia nights, and quiz shows challenge human knowledge across a wide range of topics, from canonical facts to everyday culture. In this paper, we examine whether large language models (LLMs) can perform competitively in such settings, using quiz-style questions to test them on both common and niche topics.
The paper compares Knowledge-Graph Based Augmentation (Graph-RAG) with Retrieval-Augmented Generation (RAG) for answering culturally specific questions. Using the LatamQA dataset, Graph-RAG, built automatically from Wikipedia via KGGen, matches RAG performance and reduces the base LLM’s error by 72% with a standard KG and 78% with a benchmark-aware variant. The approach also transfers zero‑shot to Portuguese, showing multilingual applicability.
By Pablo Poulenard, Yannis Karmim, Valentin Barri\`ere