The paper evaluates the tablet‑2 long‑term memory engine on multilingual text benchmarks and cross‑lingual photo retrieval without lexical matching. Tablet‑2 achieves high accuracy on LongMemEval‑S (95.7%) and moderate accuracy on BEAM‑1M (67.5%), with minimal variance across runs. In multimodal tests, it outperforms BM25 on image‑cell recall and shows significant language‑dependent performance gaps, especially for low‑resource languages.
By Sunwoo Kim
arXiv:2602.22453v4 Announce Type: replace
Abstract: Retrieval heads, a subset of attention heads in Transformers, were studied in English, showing its crucial role in retrieving information from the...
By Shaswat Patel, Vishvesh Trivedi, Yue Han, Yihuai Hong, Eunsol Choi
arXiv:2606. 16494v1 Announce Type: cross Abstract: Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditioning a reader on passages retrieved from a Wikipedia-scale knowledge base.
By Jieyuan Liu, Jianyang Gu, Shijie Chen, Jefferson Chen, Zhen Wang
arXiv:2604.27724v2 Announce Type: replace
Abstract: Medical retrieval-augmented generation (RAG) systems typically operate on text chunks extracted from biomedical literature, discarding the rich vis...
By Xupeng Chen, Binbin Shi, Chenqian Le, Jiaqi Zhang, Kewen Wang, Ran Gong, Jinhan Zhang, Chihang Wang
arXiv:2607. 16848v1 Announce Type: cross Abstract: Long-term memory is becoming a core component of LLM agents, but most memory benchmarks evaluate conversations or compact summaries, while research agents need to restore evidence from full scientific papers.
By Maksim Sheverev, David Finkelstein, Sergey Nikolenko
arXiv:2606. 04240v1 Announce Type: cross Abstract: Retrieval over visually-rich documents, pages that interleave text with figures, tables, and charts, is essential for multimodal retrieval-augmented generation, yet most retrievers still discard the visual channel.
By Jingbiao Mei
arXiv:2511. 04919v3 Announce Type: replace Abstract: Processing long documents with large language models (LLMs) is expensive: a single query over a 100K-token document can cost from tens of cents to over a dollar in API fees, depending on the model, and memory grows linearly with context length.
By Chandra Vamsi Krishna Alla, Harish Naidu Gaddam, Manohar Kommi, Sheikh Nazib Ahmed
MIDR (Multimodal Indexing for Document Retrieval) is a training‑free framework that enriches document indexes by converting rendered pages into verified textual fields with a multimodal LLM, then indexing those fields with BM25F and optionally fusing with dense retrieval. By shifting multimodal reasoning to index time, MIDR enables text‑centric serving while retaining multimodal evidence, achieving a 23.0% relative gain over BM25 on ViDoRe V3 and outperforming ColQwen2.5 on several domains with significantly smaller index memory and lower query latency.
By Debanjan Mahata, Atharva Tendle, Daniel Preotiuc-Pietro, Yong Zhuang, Ozan Irsoy
arXiv:2610.02926v1 Announce Type: new
Abstract: Standard factual benchmarks assume clean monolingual prompts and exact-match scoring, two assumptions that break simultaneously in real-world multiling...
By Riju Marwah, Ritvik Garimella, Khusham Bansal, Atishay Jain, Amit Sheth
arXiv:2606. 29914v1 Announce Type: cross Abstract: Agent memory systems are increasingly evaluated against RAG and full-context baselines, but reported gains often mix changes in the memory method with changes in the language model, embedding model, or retrieval pipeline, making it unclear what is actually being measured.
By Kuan Wang
arXiv:2610.18420v1 Announce Type: cross
Abstract: We trained Agaram 2.5B, a 2.5-billion-parameter Tamil-English language model, from scratch on about 50 billion tokens (58% Tamil, the rest English, m...
By Ramchand Kumaresan, Madhusudhanan G, Kaushik Kezendirane
arXiv:2610.18818v2 Announce Type: cross
Abstract: Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings. For machine translation of low-resource langu...
By Divyanshu Bathla, Parag Singla, Jitin Singla