arXiv:2604.01417v1 Announce Type: cross
Abstract: We present ReFormeR, a pattern-guided approach for query reformulation. Instead of prompting a language model to generate reformulations of a query directly, ReFormeR first elicits short reformulation …
arXiv:2604.01965v1 Announce Type: cross
Abstract: Scientific knowledge discovery increasingly relies on large language models, yet many existing scholarly assistants depend on proprietary systems with tens or hundreds of billions of parameters. Such r…
arXiv:2604.01733v1 Announce Type: cross
Abstract: Retrieval-Augmented Generation (RAG) systems critically depend on retrieval quality, yet no systematic comparison of modern retrieval methods exists for heterogeneous documents containing both text and…
arXiv:2604.01957v1 Announce Type: new
Abstract: Machine-translated benchmark datasets reduce costs and offer scale, but noise, loss of structure, and uneven quality weaken confidence. What matters is not merely whether we can translate, but also wheth…
arXiv:2604.02091v1 Announce Type: new
Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation. However, current reranking models are typically optimized on static human annotated relevance labels in iso…
arXiv:2604.01195v2 Announce Type: replace
Abstract: Search agents, which integrate language models (LMs) with web search, are becoming crucial for answering complex user queries. Constructing training datasets for deep research tasks, involving multi-…
arXiv:2602.22732v3 Announce Type: replace-cross
Abstract: Generative recommendation has recently attracted widespread attention in industry due to its potential for scaling and stronger model capacity. However, deploying real-time generative recommend…
arXiv:2604.01264v1 Announce Type: cross
Abstract: Medical imaging techniques, especially Magnetic Resonance Imaging (MRI), are accepted as the gold standard in the diagnosis and treatment planning of neurological diseases. However, the manual analysis…
arXiv:2601.05352v2 Announce Type: replace
Abstract: Federated learning (FL) has emerged as a transformative distributed learning paradigm, enabling multiple clients to collaboratively train a global model under the coordination of a central server wit…
arXiv:2604.00513v2 Announce Type: replace-cross
Abstract: With the rapid growth of e-commerce, exploring general representations rather than task-specific ones has attracted increasing attention. Although recent multimodal large language models (MLLMs…