arXiv:2503.12797v3 Announce Type: replace-cross Abstract: Knowledge-Intensive Visual Grounding (KVG) requires models to localize objects using fine-grained, domain-specific entity names rather than generic referring expressions. Although Multimodal Large Language Models (MLLMs) possess rich entity knowledge and strong generic grounding capabilities, they often fail to effectively utilize such knowledge when grounding specialized concepts, revealing a knowledge-grounding gap between internal knowledge and grounding predictions. To address this challenge, we propose a knowledge-aware training paradigm for KVG. Our approach first constructs knowledge-guided reasoning data to encourage models to activate domain-relevant entity knowledge during grounding, and then introduces KARL, a Knowledge-Aware Reinforcement Learning framework that adaptively modulates reward signals according to the model's estimated knowledge mastery of different entities. To facilitate systematic evaluation, we introduce KVG-Bench, a benchmark spanning 10 domains with 1.3K curated test cases covering 531 images and 882 entities. Extensive experiments show that our approach consistently outperforms a wide range of baseline models and achieves substantially stronger cross-domain generalization on unseen categories. The data, codes, and models are released at https://github.com/thunlp/KARL.

arXiv:2506.17188v2 Announce Type: replace-cross Abstract: In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-making. The paradigm employs a modular architecture of four LLM-powered agents (Master, Planner, Executor and Writer) that dynamically adapt to the full spectrum of information needs, from simple factual queries to complex multi-stage reasoning tasks. These agents collaborate dynamically through coordinated workflows to evaluate query complexity, decompose problems into executable plans, and orchestrate tool usage, task execution, and content synthesis. We systematically present key methodologies for realizing this paradigm, including task planning and tool integration, execution strategies, aligned and robust retrieval-augmented generation, and efficient LLM inference, spanning both algorithmic techniques and infrastructure-level optimizations. By providing an in-depth guide to these foundational components, this work aims to inform the development of trustworthy, adaptive, and scalable AI search systems.