7 papers
Online Dynamic Batching with Formal Guarantees for LLM Training
Dian Li, Zekun Wang, Yaoru Wang +1
Modern LLM training breaks a core assumption behind offline batch samplers: the true training cost of a sample is only observable after preprocessing, augmentation, templating, tok…
Concise Geometric Description as a Bridge: Unleashing the Potential of LLM for Plane Geometry Problem Solving
Jingyun Wang, Dian Li, Xiaohan Wang +3
Plane Geometry Problem Solving (PGPS) is a multimodal reasoning task that aims to solve a plane geometric problem based on a geometric diagram and problem textual descriptions. Alt…
Learning to Pose Problems: Reasoning-Driven and Solver-Adaptive Data Synthesis
Yongxian Wei, Yilin Zhao, Zixuan Hu +7
Data synthesis for training large reasoning models offers a scalable alternative to limited, human-curated datasets, enabling the creation of high-quality data. However, existing a…
Adaptive Global and Fine-Grained Perceptual Fusion for MLLM Embeddings Compatible with Hard Negative Amplification
Lexiang Hu, Youze Xue, Dian Li +2
Multimodal embeddings serve as a bridge for aligning vision and language, with the two primary implementations -- CLIP-based and MLLM-based embedding models -- both limited to capt…
Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning
Fanrui Zhang, Dian Li, Qiang Zhang +6
The rapid spread of multimodal misinformation on social media has raised growing concerns, while research on video misinformation detection remains limited due to the lack of large…
Improve Multi-Modal Embedding Learning via Explicit Hard Negative Gradient Amplifying
Youze Xue, Dian Li, Gang Liu
With the rapid advancement of multi-modal large language models (MLLMs) in recent years, the foundational Contrastive Language-Image Pretraining (CLIP) framework has been successfu…