21 citations · 33 across the 34 of their papers we have counts for
36 papers
TopoAgent: A Self-Evolving Topological Agent for Multimodal Scientific Reasoning
Mingze Xu, Yinghui Li, Jiayi Kuang +5
While Multimodal Large Language Models (MLLMs) excel in general tasks, rigorous scientific reasoning remains challenging due to the limitations of monolithic, linear planning. Such…
Scaling the Scaling Logic: Agentic Meta-Synthesis of Logic Reasoning
Bowen Liu, Zhi Wu, Runquan Xie +2
Reinforcement Learning from Verifiable Rewards (RLVR) is bottlenecked by data: existing synthesis pipelines rely on expert-written code or fixed templates, confining growth to inst…
Hybrid-Tower: Fine-grained Pseudo-query Interaction and Generation for Text-to-Video Retrieval
Bangxiang Lan, Ruobing Xie, Ruixiang Zhao +4
The Text-to-Video Retrieval (T2VR) task aims to retrieve unlabeled videos by textual queries with the same semantic meanings. Recent CLIP-based approaches have explored two framewo…
Adaptive Termination for Multi-round Parallel Reasoning: An Universal Semantic Entropy-Guided Framework
Zenan Xu, Zexuan Qiu, Guanhua Huang +9
Recent advances in large language models (LLMs) have accelerated progress toward artificial general intelligence, with inference-time scaling emerging as a key technique. Contempor…
Fighting Fire with Fire (F3): A Training-free and Efficient Visual Adversarial Example Purification Method in LVLMs
Yudong Zhang, Ruobing Xie, Yiqing Huang +5
Recent advances in large vision-language models (LVLMs) have showcased their remarkable capabilities across a wide range of multimodal vision-language tasks. However, these models…
The Security Threat of Compressed Projectors in Large Vision-Language Models
Yudong Zhang, Ruobing Xie, Xingwu Sun +4
The choice of a suitable visual language projector (VLP) is critical to the successful training of large visual language models (LVLMs). Mainstream VLPs can be broadly categorized…
The Climb Carves Wisdom Deeper Than the Summit: On the Noisy Rewards in Learning to Reason
Ang Lv, Ruobing Xie, Xingwu Sun +2
Recent studies on post-training large language models (LLMs) for reasoning through reinforcement learning (RL) typically focus on tasks that can be accurately verified and rewarded…
Low-hallucination Synthetic Captions for Large-Scale Vision-Language Model Pre-training
Xinsong Zhang, Yarong Zeng, Xinting Huang +4
In recent years, the field of vision-language model pre-training has experienced rapid advancements, driven primarily by the continuous enhancement of textual capabilities in large…
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
Tencent Hunyuan Team, Ao Liu, Botong Zhou +248
As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mam…
QAVA: Query-Agnostic Visual Attack to Large Vision-Language Models
Yudong Zhang, Ruobing Xie, Jiansheng Chen +3
In typical multimodal tasks, such as Visual Question Answering (VQA), adversarial attacks targeting a specific image and question can lead large vision-language models (LVLMs) to p…
Large Language Model Empowered Recommendation Meets All-domain Continual Pre-Training
Haokai Ma, Yunshan Ma, Ruobing Xie +5
Recent research efforts have investigated how to integrate Large Language Models (LLMs) into recommendation, capitalizing on their semantic comprehension and open-world knowledge f…
TransMamba: A Sequence-Level Hybrid Transformer-Mamba Language Model
Yixing Li, Ruobing Xie, Zhen Yang +8
Transformers are the cornerstone of modern large language models, but their quadratic computational complexity limits efficiency in long-sequence processing. Recent advancements in…
Multi-Grained Patch Training for Efficient LLM-based Recommendation
Jiayi Liao, Ruobing Xie, Sihang Li +4
Large Language Models (LLMs) have emerged as a new paradigm for recommendation by converting interacted item history into language modeling. However, constrained by the limited con…
Autonomy-of-Experts Models
Ang Lv, Ruobing Xie, Yining Qian +5
Mixture-of-Experts (MoE) models mostly use a router to assign tokens to specific expert modules, activating only partial parameters and often outperforming dense models. We argue t…
Scaling Laws for Floating Point Quantization Training
Xingwu Sun, Shuaipeng Li, Ruobing Xie +13
Low-precision training is considered an effective strategy for reducing both training and downstream inference costs. Previous scaling laws for precision mainly focus on integer qu…
Enhancing Contrastive Learning Inspired by the Philosophy of "The Blind Men and the Elephant"
Yudong Zhang, Ruobing Xie, Jiansheng Chen +3
Contrastive learning is a prevalent technique in self-supervised vision representation learning, typically generating positive pairs by applying two data augmentations to the same…
Mitigating Hallucination in Multimodal Large Language Model via Hallucination-targeted Direct Preference Optimization
Yuhan Fu, Ruobing Xie, Xingwu Sun +2
Multimodal Large Language Models (MLLMs) are known to hallucinate, which limits their practical applications. Recent works have attempted to apply Direct Preference Optimization (D…
DHCP: Detecting Hallucinations by Cross-modal Attention Pattern in Large Vision-Language Models
Yudong Zhang, Ruobing Xie, Xingwu Sun +5
Large vision-language models (LVLMs) have demonstrated exceptional performance on complex multimodal tasks. However, they continue to suffer from significant hallucination issues,…
More Expressive Attention with Negative Weights
Ang Lv, Ruobing Xie, Shuaipeng Li +5
We propose a novel attention mechanism, named Cog Attention, that enables attention weights to be negative for enhanced expressiveness, which stems from two key factors: (1) Cog At…
Exploring Forgetting in Large Language Model Pre-Training
Chonghua Liao, Ruobing Xie, Xingwu Sun +2
Catastrophic forgetting remains a formidable obstacle to building an omniscient model in large language models (LLMs). Despite the pioneering research on task-level forgetting in L…
Lossless KV Cache Compression to 2%
Zhen Yang, J. N. Han, Kan Wu +4
Large language models have revolutionized data processing in numerous domains, with their ability to handle extended context reasoning receiving notable recognition. To speed up in…
RosePO: Aligning LLM-based Recommenders with Human Values
Jiayi Liao, Xiangnan He, Ruobing Xie +5
Recently, there has been a growing interest in leveraging Large Language Models (LLMs) for recommendation systems, which usually adapt a pre-trained LLM to the recommendation scena…
Continuous Speech Tokenizer in Text To Speech
Yixing Li, Ruobing Xie, Xingwu Sun +2
The fusion of speech and language in the era of large language models has garnered significant attention. Discrete speech token is often utilized in text-to-speech tasks for speech…
Magnifier Prompt: Tackling Multimodal Hallucination via Extremely Simple Instructions
Yuhan Fu, Ruobing Xie, Jiazhen Liu +4
Hallucinations in multimodal large language models (MLLMs) hinder their practical applications. To address this, we propose a Magnifier Prompt (MagPrompt), a simple yet effective m…