20 citations · 39 across the 46 of their papers we have counts for
8 papers · 1 filter
Beyond the Remembered World: Predictive 4D Belief for Persistent Navigation in Evolving Worlds
Mingjian Gao, Zhaocheng Li, Haoyang Huang +7
Persistent spatial memory enables embodied agents to navigate familiar environments across repeated visits. However, targets may move while unobserved, including during navigation,…
SCOPE: Evolving Symbolic World for Planning in Open-Ended Environments
Yundaichuan Zhan, Minghe Gao, Zhongqi Yue +7
Recent works have explored integrating Vision-Language Models (VLMs) with classical planners that rely on symbolic representations of planning problems to generate long-horizon pla…
Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration
Weile Chen, Bingchen Miao, Qifan Yu +6
Recent advances in Multimodal Large Language Models (MLLMs) have led to promising progress in web agents. However, existing web agents often rely on handcrafted execution pipelines…
Draft-Thinking: Learning Efficient Reasoning in Long Chain-of-Thought LLMs
Jie Cao, Tianwei Lin, Zhenxuan Fan +7
Long chain-of-thought~(CoT) reasoning improves the problem-solving capability of large reasoning models~(LRMs), but often incurs substantial inference cost and overthinking. Existi…
Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs
Zhaoyu Fan, Kaihang Pan, Mingze Zhou +7
Knowledge editing enables multimodal large language models (MLLMs) to efficiently update outdated or incorrect information. However, existing benchmarks primarily emphasize cogniti…
Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework
Jiang Liu, Bolin Li, Haoyuan Li +13
Efficient multimodal large language models (EMLLMs), in contrast to multimodal large language models (MLLMs), reduce model size and computational costs and are often deployed on re…
AlignLLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction Curation
Hongzhe Huang, Jiang Liu, Zhewen Yu +8
Recent advances in Multi-modal Large Language Models (MLLMs), such as LLaVA-series models, are driven by massive machine-generated instruction-following data tuning. Such automatic…
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
Wenqiao Zhang, Tianwei Lin, Jiang Liu +10
Recent advancements indicate that scaling up Multimodal Large Language Models (MLLMs) effectively enhances performance on downstream multimodal tasks. The prevailing MLLM paradigm,…