7 papers
AdaTutoRank: Learning to Rerank Document Sets via Adaptive Tutoring Optimization for RAG and Deep Research
Kailin Jiang, Lei Liu, Jian Xi +6
Document rerankers determine what evidence reaches the downstream model in RAG and deep research, yet mainstream rerankers select by relevance matching, and individually relevant d…
Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking
Kailin Jiang, Lei Liu, Jian Xi +7
As large language models and AI agents become the primary consumers of search results, document set quality determines the upper bound of downstream generation. Yet existing evalua…
Chain Of Interaction Benchmark (COIN): When Reasoning meets Embodied Interaction
Xianhao Wang, Xiaojian Ma, Haozhe Hu +7
Generalist embodied agents must perform interactive, causally-dependent reasoning, continually interacting with the environment, acquiring information, and updating plans to solve…
MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models
Kailin Jiang, Ning Jiang, Yuntao Du +8
Large Multimodal Models (LMMs) encode rich factual knowledge via cross-modal pre-training, yet their static representations struggle to maintain an accurate understanding of time-s…
KORE: Enhancing Knowledge Injection for Large Multimodal Models via Knowledge-Oriented Controls
Kailin Jiang, Hongbo Jiang, Ning Jiang +7
Large Multimodal Models encode extensive factual knowledge in their pre-trained weights. However, its knowledge remains static and limited, unable to keep pace with real-world deve…
StyleAR: Customizing Multimodal Autoregressive Model for Style-Aligned Text-to-Image Generation
Yi Wu, Lingting Zhu, Shengju Qian +4
In the current research landscape, multimodal autoregressive (AR) models have shown exceptional capabilities across various domains, including visual understanding and generation.…
When Large Multimodal Models Confront Evolving Knowledge: Challenges and Explorations
Kailin Jiang, Yuntao Du, Yukai Ding +7
Large Multimodal Models (LMMs) store vast amounts of pretrained knowledge but struggle to remain aligned with real-world updates, making it difficult to avoid capability degradatio…