「retrieval」の検索結果
20 件Previous research shows that vision-language (VL) models struggle to understand fine-grained image details and
Dhārā Bench is a retrieval benchmark for Indian Knowledge Systems (IKS), with 325 questions in 12 categories a
Scaling depth is a key route to stronger large language models, but Pre-LN architectures often suffer from dep
This paper studies retrieval-augmented generation (RAG), which leverages external knowledge to improve the per
Large language models are increasingly consulted for religious knowledge, including the recall of specific Had
Retrieval-augmented generation (RAG) extends large language models with external evidence, yet relevant eviden
Cloth-changing person re-identification (CC-ReID) is still very challenging, and no method clearly dominates:
医学画像に対する疾患検出モデルを開発し、臨床現場で早期検出と迅速な介入を容易にすることを目的としたフレームワークを提案します。
Decision models target structured tasks by directly predicting choices or scores, avoiding autoregressive deco
Retrieval-augmented generation (RAG) lets a large language model (LLM) answer questions by retrieving relevant
Linear attention scales linearly with sequence length, but its finite-state memory can limit long-context retr
Vision-Language-Action (VLA) models have demonstrated promising capabilities in robotic manipulation by integr
An observer sees only part of the surrounding world, yet must keep track of what lies beyond the current view.
Proactive agents must decide whether, when, and how far to assist without an explicit request. Existing evalua
An effective agent memory system should adapt to a user's history and task requirements. Human-annotated super
Retrieval-augmented generation commonly serves separate models for reranking and answering, duplicating model
Financial agents must form forecasts and investment judgments from decision-time evidence, with predictions ev
Large Language Models (LLMs) are increasingly used in multi-turn agentic workloads that require sustained inte
Locating relevant evidence in long inputs is a core capability of Multimodal Large Language Models. Prior stud
Reinforcement learning (RL) is an effective way to strengthen the long-context reasoning of large language mod