1 citations · 2 across the 9 of their papers we have counts for
8 papers · 1 filter
TRIAD: Benchmarking Omni-Modal Ambiguity in Multimodal Large Language Models
Zhaolu Kang, Yidi Wang, Yile Li +4
Ambiguity is a central feature of natural communication: a sentence, image, or sound may remain underspecified until evidence from the other channels is considered. Yet current mul…
RECAP: Looking Once Is Not Enough for Vision-Language Reasoning
Zhaolu Kang, Tailong Luo, Chenxin Li +13
Long chain-of-thought reasoning improves vision-language models (VLMs), but it also exposes a temporal grounding failure: as generation unfolds, models may drift from image evidenc…
DRILL: Training World Models to Improve Policies, Not Predict Pixels
Zhaolu Kang, Tailong Luo, Lei Wei +9
World models are increasingly used to reduce environment interaction when post-training vision-language-action (VLA) policies with reinforcement learning. Yet they are typically tr…
Behavior Pack Optimization for Video MLLM Post-Training
Zhaolu Kang, Shiyu Liu, Tailong Luo +12
Video multimodal large language models (MLLMs) keep climbing video question answering benchmarks, yet shuffling the frames, masking the segment that supports the answer, or occludi…
Beyond Surface Imitation: Contrastive Modeling for Reasoning Path Alignment in Multimodal In-Context Learning
Mingbo Yang, Wenqiang Wang, Zhaolu Kang +4
In-context learning (ICL) is widely used in multimodal large language models (MLLMs) and achieves strong performance across a wide range of multimodal tasks. However, existing mult…
Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning
Zhaolu Kang, Meixin Wu, Yu Xue +6
Omni-modal large language models are increasingly evaluated on clean text--vision--audio inputs, where every channel is present, synchronized, and readily interpretable. Such score…
COMET: Contrastive Motion-Enhanced Temporal Reasoning for Video Multimodal Large Language Models
Chenghua Zhu, Zhaolu Kang, Qifan Shi +8
Video multimodal large language models have advanced significantly, yet fine-grained motion-temporal understanding remains fragile. The core bottleneck is not only sparse frame sam…
Penny Wise, Pixel Foolish: Bypassing Price Constraints in Multimodal Agents via Visual Adversarial Perturbations
Jiachen Qian, Zhaolu Kang
The rapid proliferation of Multimodal Large Language Models (MLLMs) has enabled mobile agents to execute high-stakes financial transactions, but their adversarial robustness remain…