Retrieval & RAG papers
What AI researchers are reading: the papers the Hugging Face community picks each day, and new arXiv submissions in the directions we follow. Titles and abstracts are arXiv’s; every paper links to arXiv.
RS-JEPA: Remote Sensing Joint-Embedding Predictive Architecture with Scan-Path Views for Cross-Sensor Image Retrieval
Tanishq Rachamalla, Srishti Kaushik, Muhammad Attique Khan et al.
Cross-sensor remote sensing image retrieval aims to find scenes with the same content across heterogeneous sensors, such as SAR and optical or panchromatic (PAN) and multispectral (MS) imagery, without manual labels. Existing self-supervised methods either reconstruct sensor-specific pixel detail, align each image…
FERN: Cross-Benchmark Table Retrieval for Text-to-SQL with Frozen Retrievers and Learned Ranking
Arjun Rao, Nishant Gopinath
Text-to-SQL systems over large pooled catalogs must first retrieve the tables a question needs. We compare against the published table recall of RASL, which uses Claude 3.5 models to extract keywords and select tables, on pooled BIRD, Spider, and Fiben catalogs. FERN (Feature-Engineered Ranking with No generative…
CLADE: Category-Level Answering from Deficient Evidence in Retrieval-Augmented Language Models
S M Rayeed, Charles V. Stewart, Yao Ma
Retrieval-augmented systems often retrieve passages that are accurate and relevant but state the answer at a coarser level of granularity than the question requires. For example, a question may ask which specific disease a drug treats (familial hyperlipidemia), while the retrieved passage mentions the broader…
Market-Conditioned JEPA: Learning from Unpredictable Market Shocks
Yuqi Wang, Fengyuan Liu, Mukai Li et al.
Much of the variation in financial time series is driven by market-wide shocks that cannot be predicted from an asset's own history. Per-asset self-supervised learning treats this variation as irreducible noise. Under a latent factor model, we show that conditioning prediction on the realized market factor makes…
onóna: Twin Embeddings with Tied Unembeddings
Cheng Luo, Xinnan Dai, Zhongzhu Zhou et al.
Weight tying reduces the parameter cost of language models, but the usual tied output head is limited to a single softmax. We introduce onóna, a two-table parameterization that increases output expressiveness while preserving input-output weight sharing. The Transformer takes the average of two embedding tables as…
e-means: outperforming k-means (in one-tenth the epochs)
Swann Bessa, Pierre-Emmanuel Mazaré, Gergely Szilvasy et al.
Lloyd's k-means algorithm is a long-running standard for clustering, with applications in data analysis, retrieval, quantization and data curation. In this paper, we introduce an exponential moving average optimization procedure for k-means with mini-batch updates, powered by modern deep learning machinery and new…
SkinMind: Equipping Agentic RAG with Human-Inspired Graph Probing for Reliable Dermatology AI
Jing Hao, Weinan Zhou, Zhihong Lu et al.
Reliable dermatology AI requires access to verifiable evidence distributed across heterogeneous textual and visual sources. However, conventional retrieval-augmented generation (RAG) typically relies on one-shot retrieval from flat corpora, limiting its ability to adapt retrieval granularity, explore multi-hop…
Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching
Chuangtao Ma, Sriom Chakrabarti, Arijit Khan et al.
Traditional similarity-based schema matching methods fail to resolve semantic ambiguities and conflicts in complex, domain-specific mappings due to a lack of commonsense and domain-specific knowledge. In parallel, large language models (LLMs) without sufficient background knowledge tend to hallucinate, further…
TRIO: Task Refinement with Individual Optimization for Retrieval-Augmented Generation
Yingtao Ren, Xiao Luo, Yiwei Fu et al.
This paper studies retrieval-augmented generation (RAG), which leverages external knowledge to improve the performance of language models. Despite the remarkable progress, current RAG approaches are still far from satisfactory due to inadequate retrieval and potential hallucinations. Towards this end, we propose a…