6 papers
Reason Before You Retrieve: Agentic Planning for Multi-modal RAG
Tianyu Yang, Shir Simon, Zhenzhen Li +2
Multimodal retrieval-augmented generation (mRAG) aims to answer image-text queries with external knowledge, but most existing systems still retrieve directly from raw multimodal in…
Scalable Token-Level Hallucination Detection in Large Language Models
Rui Min, Tianyu Pang, Chao Du +2
Large language models (LLMs) have demonstrated remarkable capabilities, but they still frequently produce hallucinations. These hallucinations are difficult to detect in reasoning-…
Route Before Retrieve: Activating Latent Routing Abilities of LLMs for RAG vs. Long-Context Selection
Yiwen Chen, Kuan Li, Fuzhen Zhuang +6
Recent advances in large language models (LLMs) have expanded the context window to beyond 128K tokens, enabling long-document understanding and multi-source reasoning. A key chall…
CARV: A Diagnostic Benchmark for Compositional Analogical Reasoning in Multimodal LLMs
Yongkang Du, Xiaohan Zou, Minhao Cheng +1
Analogical reasoning tests a fundamental aspect of human cognition: mapping the relation from one pair of objects to another. Existing evaluations of this ability in multimodal lar…
ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization
Xixi Wu, Kuan Li, Yida Zhao +13
Large Language Model (LLM)-based web agents excel at knowledge-intensive tasks but face a fundamental conflict between the need for extensive exploration and the constraints of lim…
LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs -- No Silver Bullet for LC or RAG Routing
Kuan Li, Liwen Zhang, Yong Jiang +4
Effectively incorporating external knowledge into Large Language Models (LLMs) is crucial for enhancing their capabilities and addressing real-world needs. Retrieval-Augmented Gene…