8 citations · 17 across the 17 of their papers we have counts for
19 papers
ScenePercept: Scenario-Conditioned Perceptual Assessment of Real-World User-Generated Content Images
Shuhan Xia, Yu Chen, Xiang Wang +3
The rapid development of mobile imaging and social platforms has led to explosive growth in user-generated content images, making their systematic and comprehensive perceptual asse…
Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning
Zirui Song, Huaxing Liu, Xiang Wang +8
Prior white-box studies show that large language models can retain latent traces of target knowledge after unlearning, even when the knowledge is no longer expressed in their outpu…
CAVE: Competence-Aware Visual Boundary Evidence Alignment for Video Temporal Grounding
Wei Jia, Zhicong Lu, Yu Chen +5
Large vision-language models (LVLMs) have achieved substantial performance gains in Video Temporal Grounding (VTG) through reinforcement learning (RL). However, existing methods pr…
One Ranking, Any Budget: Matryoshka Evidence-to-Context Frame Selection for Long-Video Understanding
Wang Chen, Yu Chen, Xiang Wang +3
Frame selection is essential for applying Large Multimodal Models (LMMs) to long videos due to severe frame redundancy and limited context windows. Since the appropriate frame budg…
Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias
Zixiang Xu, Sixian Li, Huaxing Liu +4
Existing studies of LLM-as-judge scoring bias work predominantly at the input-output level: they perturb inputs, measure score deltas, and propose prompt-level mitigations. We argu…
Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts
Xiangtian Ji, Yuxin Chen, Zhengzhou Cai +3
Large language models (LLMs) display strong comprehensive abilities, yet the internal mechanisms that support these behaviors remain insufficiently understood. In this work, we sho…
Faithful-MR1: Faithful Multimodal Reasoning via Anchoring and Reinforcing Visual Attention
Changyuan Tian, Zhicong Lu, Huaxing Liu +7
Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising paradigm for advancing complex reasoning in large language models, and recent work extends RLVR to…
Reasoning Can Be Restored by Correcting a Few Decision Tokens
Changshuo Shen, Leheng Sheng, Yuxin Chen +2
Large reasoning models (LRMs) substantially outperform their base LLM counterparts on challenging reasoning benchmarks, yet it remains poorly understood where base models go wrong…
UniVLR: Unifying Text and Vision in Visual Latent Reasoning for Multimodal LLMs
Houcheng Jiang, Jiajun Fu, Junfeng Fang +4
Multimodal large language models are increasingly expected to perform thinking with images, yet existing visual latent reasoning methods still rely on explicit textual chain-of-tho…
Towards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion Modeling
Yanchen Luo, Zhiyuan Liu, Yi Zhao +6
3D molecule generation is crucial for drug discovery and material science, requiring models to process complex multi-modalities, including atom types, chemical bonds, and 3D coordi…
NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation
Zhiyuan Liu, Yanchen Luo, Han Huang +7
3D molecule generation is crucial for drug discovery and material design. While prior efforts focus on 3D diffusion models for their benefits in modeling continuous 3D conformers,…
AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models
Junfeng Fang, Houcheng Jiang, Kun Wang +5
Large language models (LLMs) often exhibit hallucinations due to incorrect or outdated knowledge. Hence, model editing methods have emerged to enable targeted knowledge updates. To…
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling
Yunshan Ma, Yingzhi He, Wenjun Zhong +3
Product bundling has been a prevailing marketing strategy that is beneficial in the online shopping scenario. Effective product bundling methods depend on high-quality item represe…
Towards 3D Molecule-Text Interpretation in Language Models
Sihang Li, Zhiyuan Liu, Yanchen Luo +5
Language Models (LMs) have greatly influenced diverse domains. However, their inherent limitation in comprehending 3D molecular structures has considerably constrained their potent…
SCTc-TE: A Comprehensive Formulation and Benchmark for Temporal Event Forecasting
Yunshan Ma, Chenchen Ye, Zijian Wu +4
Temporal complex event forecasting aims to predict the future events given the observed events from history. Most formulations of temporal complex event are unstructured or without…
MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation
Yunshan Ma, Yingzhi He, Xiang Wang +4
Bundle recommendation seeks to recommend a bundle of related items to users to improve both user experience and the profits of platform. Existing bundle recommendation models have…
Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction
Yunshan Ma, Xiaohao Liu, Yinwei Wei +3
Automatic bundle construction is a crucial prerequisite step in various bundle-aware online services. Previous approaches are mostly designed to model the bundling strategy of exis…
A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction
Ruihao Shui, Yixin Cao, Xiang Wang +1
Large language models (LLMs) have demonstrated great potential for domain-specific applications, such as the law domain. However, recent disputes over GPT-4's law evaluation raise…
MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter
Zhiyuan Liu, Sihang Li, Yanchen Luo +5
Language Models (LMs) have demonstrated impressive molecule understanding ability on various 1D text-related tasks. However, they inherently lack 2D graph perception - a critical a…