2 citations · 3 across the 15 of their papers we have counts for
15 papers
X-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment
Dongjie Fu, Di Cao, Xize Cheng +6
While large audio-language models have achieved remarkable progress in auditory perception, they still lag behind text-based large language models in deep logical reasoning, primar…
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
ATEX-CF: Attack-Informed Counterfactual Explanations for Graph Neural Networks
Yu Zhang, Sean Bin Yang, Arijit Khan +1
Counterfactual explanations offer an intuitive way to interpret graph neural networks (GNNs) by identifying minimal changes that alter a model's prediction, thereby answering "what…
GraphDancer: Training LLMs to Explore and Reason over Graphs via Curriculum Reinforcement Learning
Yuyang Bai, Zhuofeng Li, Ping Nie +3
Large language models (LLMs) increasingly rely on external knowledge to improve factuality, yet many knowledge sources on the Web, such as scholarly indexes and product catalogs, a…
HalluClean: A Unified Framework to Combat Hallucinations in LLMs
Yaxin Zhao, Yu Zhang
Large language models (LLMs) have achieved impressive performance across a wide range of natural language processing tasks, yet they often produce hallucinated content that undermi…
RPTS: Tree-Structured Reasoning Process Scoring for Faithful Multimodal Evaluation
Haofeng Wang, Yu Zhang
Large Vision-Language Models (LVLMs) excel in multimodal reasoning and have shown impressive performance on various multimodal benchmarks. However, most of these benchmarks evaluat…
Toward Robust Multilingual Adaptation of LLMs for Low-Resource Languages
Haolin Li, Haipeng Zhang, Mang Li +4
Large language models (LLMs) continue to struggle with low-resource languages, primarily due to limited training data, translation noise, and unstable cross-lingual alignment. To a…
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Weigao Sun, Jiaxi Hu, Yucheng Zhou +12
Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models. Transformer m…
X-Intelligence 3.0: Training and Evaluating Reasoning LLM for Semiconductor Display
Xiaolin Yan, Yangxing Liu, Jiazhang Zheng +53
Large language models (LLMs) have recently achieved significant advances in reasoning and demonstrated their advantages in solving challenging problems. Yet, their effectiveness in…
Agentic Robot: A Brain-Inspired Framework for Vision-Language-Action Models in Embodied Agents
Zhejian Yang, Yongchao Chen, Xueyang Zhou +8
Long-horizon robotic manipulation poses significant challenges for autonomous systems, requiring extended reasoning, precise execution, and robust error recovery across complex seq…
ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RL
Yu Zhang, Yunqi Li, Yifan Yang +7
Although chain-of-thought reasoning and reinforcement learning (RL) have driven breakthroughs in NLP, their integration into generative vision models remains underexplored. We intr…
MOSLIM:Align with diverse preferences in prompts through reward classification
Yu Zhang, Wanli Jiang, Zhengyu Yang
The multi-objective alignment of Large Language Models (LLMs) is essential for ensuring foundational models conform to diverse human preferences. Current research in this field typ…
Multimodal Human-AI Synergy for Medical Imaging Quality Control: A Hybrid Intelligence Framework with Adaptive Dataset Curation and Closed-Loop Evaluation
Zhi Qin, Qianhui Gui, Mouxiao Bian +14
Medical imaging quality control (QC) is essential for accurate diagnosis, yet traditional QC methods remain labor-intensive and subjective. To address this challenge, in this study…
A Survey on Post-training of Large Language Models
Guiyao Tie, Zeli Zhao, Dingjie Song +23
The emergence of Large Language Models (LLMs) has fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational system…
Building Decision Making Models Through Language Model Regime
Yu Zhang, Haoxiang Liu, Feijun Jiang +2
We propose a novel approach for decision making problems leveraging the generalization capabilities of large language models (LLMs). Traditional methods such as expert systems, pla…