11 citations · 33 across the 42 of their papers we have counts for
20 papers · 1 filter
InterTab: Interleaved Visual-Structure Alignment for Multi-Modal Table Reasoning
Hanqian Li, Sirui Huang, Chen Ling +12
Table images preserve structural information that are often lost in text serialization, and reasoning over them requires locating relevant rows, columns, and cells step by step. Cu…
AnchorFold: A Focus-Then-Fold Framework via Recursive Attention Propagation for Efficient Multi-Vector Visual Document Retrieval
Haoyu Zuo, Yibo Yan, Xin Zou +4
Multi-vector vision-language retrievers enable fine-grained Visual Document Retrieval (VDR) through late interaction, but storing and scoring hundreds of visual patch embeddings pe…
Should Missing Modalities Always Be Necessary to Repair for Multi-modal Sentiment Analysis?
Yubo Gao, Haotian Wu, Xiaoyu Xu +9
Existing methods for multimodal sentiment analysis (MSA) under missing modalities usually follow a repair-first paradigm. We revisit this assumption and ask: \emph{should every mis…
Clearer Sight, Fewer Lies: Oriented Pickup Preference Optimization for Multimodal Hallucination Mitigation
Xin Zou, Haolin Deng, Yibo Yan +5
Multimodal Large Language Models (MLLMs) are prone to hallucination as their generation preferences are insufficiently calibrated to visual evidence, causing them to fall back on l…
Consistency as Inductive Bias: Learning Cross-View Invariance for Robust Multimodal Reasoning
Xin Zou, Haolin Deng, Yibo Yan +6
Inductive biases steer learning toward generalizable solutions by encoding task structure. In this work, we identify a crucial missing bias in MLLMs: cross-view consistency, \texti…
Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs
Yubo Gao, Haotian Wu, Hong Chen +8
Chain-of-Thought (CoT) has significantly enhanced LLM reasoning, yet often incurs substantial computational overhead due to "overthinking": generating excessively long rationales w…
Learning from Fine-Grained Visual Discrepancies: Mitigating Multimodal Hallucinations via In-Context Visual Contrastive Optimization
Haolin Deng, Xin Zou, Zhiwei Jin +3
Multimodal hallucination remains a persistent challenge for Vision-Language Models (VLMs). Standard textual Direct Preference Optimization (DPO) often fails to mitigate it due to a…
DISA: Offline Importance Sampling for Distribution-Matching LLM-RL
Shaobo Wang, Yujie Chen, Yafeng Sun +9
Modern reasoning agents are increasingly evaluated on their ability to generate multiple valid solution paths, plans, or tool-use traces for a given input. Standard reward-maximizi…
NestedKV: Nested Memory Routing for Long-Context KV Cache Compression
Hong Chen, Xiang Liu, Yubo Gao +5
Long-context language models are limited by the memory footprint of the key-value (KV) cache. Existing training-free KV compression methods usually rank tokens by one importance si…
SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness
Jiahao Huo, Wenjie Qu, Yibo Yan +5
Semantic-level watermarking (SWM) improves robustness against text modifications by treating sentences as the basic unit. However, robustness to paragraph-level paraphrasing remain…
Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval
Yibo Yan, Mingdong Ou, Yi Cao +5
Multi-vector models dominate Visual Document Retrieval (VDR) due to their fine-grained matching capabilities, but their high storage and computational costs present a major barrier…
Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation
Kening Zheng, Wei-Chieh Huang, Jiahao Huo +9
Mixture-of-Experts (MoE) models exhibit striking performance disparities across languages, yet the internal mechanisms driving these gaps remain poorly understood. In this work, we…
AndroTMem: From Interaction Trajectories to Anchored Memory in Long-Horizon GUI Agents
Yibo Shi, Jungang Li, Linghao Zhang +25
Long-horizon GUI agents are a key step toward real-world deployment, yet effective interaction memory under prevailing paradigms remains under-explored. Replaying full interaction…
LatentGeo: Learnable Auxiliary Constructions in Latent Space for Multimodal Geometric Reasoning
Haiying Xu, Zihan Wang, Song Dai +3
Despite recent advances in multimodal reasoning, representing auxiliary geometric constructions remains a fundamental challenge for multimodal large language models (MLLMs). Such c…
Beyond the Grid: Layout-Informed Multi-Vector Retrieval with Parsed Visual Document Representations
Yibo Yan, Mingdong Ou, Yi Cao +6
Harnessing the full potential of visually-rich documents requires retrieval systems that understand not just text, but intricate layouts, a core challenge in Visual Document Retrie…
Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval
Yibo Yan, Jiahao Huo, Guanbo Feng +12
With the rapid proliferation of multimodal information, Visual Document Retrieval (VDR) has emerged as a critical frontier in bridging the gap between unstructured visually rich da…
Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework
Yibo Yan, Mingdong Ou, Yi Cao +5
Visual Document Retrieval (VDR), which aims to retrieve relevant pages within vast corpora of visually-rich documents, is of significance in current multimodal retrieval applicatio…
Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering
Shuliang Liu, Songbo Yang, Dong Fang +7
Object hallucination critically undermines the reliability of Multimodal Large Language Models, often stemming from a fundamental failure in cognitive introspection, where models b…
CausalEmbed: Auto-Regressive Multi-Vector Generation in Latent Space for Visual Document Embedding
Jiahao Huo, Yu Huang, Yibo Yan +7
Although Multimodal Large Language Models (MLLMs) have shown remarkable potential in Visual Document Retrieval (VDR) through generating high-quality multi-vector embeddings, the su…
Distilling the Thought, Watermarking the Answer: A Principle Semantic Guided Watermark for Large Reasoning Models
Shuliang Liu, Xingyu Li, Hongyi Liu +6
Reasoning Large Language Models (RLLMs) excelling in complex tasks present unique challenges for digital watermarking, as existing methods often disrupt logical coherence or incur…