Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.
arXiv:2501.08547v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have been widely adopted for their ability to compute expressive node representations in graph datasets. However...
arXiv:2503.24111v4 Announce Type: replace-cross Abstract: Graph Neural Networks (QGNNs) offer a promising approach to combining quantum computing with graph-structured data processing. While classica...
arXiv:2603.01590v2 Announce Type: replace-cross Abstract: Content-driven platforms such as Xiaohongshu often leverage click-through rate (CTR) prediction models for recommendation. However, these mod...
arXiv:2609.22099v1 Announce Type: new Abstract: Cooking is a complex process that transforms raw ingredients into delicious and nutritious dishes, yet the recipes that encode this process remain larg...
arXiv:2609.22231v1 Announce Type: new Abstract: Long-horizon interactions with LLM-based assistants require memory systems that preserve and update user states, preferences, and interaction histories...
arXiv:2609.22796v1 Announce Type: new Abstract: Dialectal Arabic machine translation (MT) remains challenging despite recent progress in Arabic language technologies, particularly because effective t...
arXiv:2609.23056v1 Announce Type: new Abstract: Agentic retrieval-augmented generation (RAG) enables language models to adapt retrieval based on previously retrieved evidence, but it remains unclear...
arXiv:2609.23231v1 Announce Type: new Abstract: Cross-lingual information retrieval (CLIR) is increasingly important in multi-national industries, where critical technical evidence may exist in a dif...
arXiv:2609.23267v1 Announce Type: new Abstract: The visual modality, i.e., images, plays a key role in multi-modal entity alignment (MMEA). Existing approaches often directly fuse the image with othe...
arXiv:2609.23880v1 Announce Type: new Abstract: Temporal Information Retrieval (TIR) has been increasingly critical given the rise of Retrieval-Augmented Generation (RAG). Since temporally mismatched...
arXiv:2609.24372v1 Announce Type: new Abstract: In-context learning (ICL) based on large language models (LLMs) has shown promising potential in alleviating performance bottlenecks caused by the limi...
arXiv:2609.24932v1 Announce Type: new Abstract: Despite the success of neural models in natural language processing, their black-box nature limits interpretability and conceals the linguistic phenome...
arXiv:2509.22030v2 Announce Type: replace Abstract: This paper examines how outliers, often dismissed as noise in topic modeling, can act as weak signals of emerging topics in dynamic news corpora. U...
arXiv:2601.03981v3 Announce Type: replace Abstract: To efficiently combat the spread of LLM-generated misinformation in the news domain, we present RADAR, a Retrieval-Augmented Detector with Adversar...
arXiv:2603.18358v2 Announce Type: replace Abstract: Outliers in dynamic topic modeling are typically treated as noise, yet we show that some can serve as early signals of emerging topics. We introduc...
arXiv:2605.25920v2 Announce Type: replace Abstract: While large language models (LLMs) augmented with agentic search capabilities show promise for legal reasoning, they overlook a fundamental constra...
arXiv:2608.17153v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) improves large language models by grounding them in external evidence, but this exposes them to knowledge-pois...
arXiv:2609.22351v1 Announce Type: new Abstract: Open-vocabulary 3D Scene Graphs (3DSGs) ground each object node in a vision-language embedding, yet they record every entry as equally certain, so a ro...
arXiv:2609.22562v1 Announce Type: new Abstract: Multi-vector visual document retrieval (VDR) models such as ColPali and ColNomic achieve strong accuracy by representing each document with hundreds to...
arXiv:2609.23248v1 Announce Type: new Abstract: A fundamental challenge in deploying vision models is domain shift, which arises when training and test data follow different distributions, leading to...