57 citations · 524 across the 176 of their papers we have counts for
31 papers · 1 filter
HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling
Chulun Zhou, Chunkang Zhang, Guoxin Yu +4
Multi-step retrieval-augmented generation (RAG) has become a widely adopted strategy for enhancing large language models (LLMs) on tasks that demand global comprehension and intens…
Efficient Covariance Estimation for Sparsified Functional Data
Sijie Zheng, Fandong Meng, Jie Zhou
Motivated by recent work involving the analysis of leveraging spatial correlations in sparsified mean estimation, we present a novel procedure for constructing covariance estimator…
Conan: Progressive Learning to Reason Like a Detective over Multi-Scale Visual Evidence
Kun Ouyang, Yuanxin Liu, Linli Yao +5
Video reasoning, which requires multi-step deduction across frames, remains a major challenge for multimodal large language models (MLLMs). While reinforcement learning (RL)-based…
UME-R1: Exploring Reasoning-Driven Generative Multimodal Embeddings
Zhibin Lan, Liqiang Niu, Fandong Meng +2
The remarkable success of multimodal large language models (MLLMs) has driven advances in multimodal embeddings, yet existing models remain inherently discriminative, limiting thei…
Continuous Autoregressive Language Models
Chenze Shao, Darren Li, Fandong Meng +1
The efficiency of large language models (LLMs) is fundamentally limited by their sequential, token-by-token generation process. We argue that overcoming this bottleneck requires a…
Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning
Xue Zhang, Yunlong Liang, Fandong Meng +5
Large Reasoning Models (LRMs) have achieved remarkable performance on complex reasoning tasks by adopting the ``think-then-answer'' paradigm, which enhances both accuracy and inter…