1 citations · 1 across the 12 of their papers we have counts for
15 papers
Asking Less, Adapting Better: Feedback-Efficient Test-Time Adaptation for Vision-and-Language Navigation
Zixu Wang, Huaquan Zhang, Shoukai Xu +3
Vision-and-Language Navigation (VLN) requires agents to follow instructions and navigate to target locations. However, current VLN agents often suffer severe performance degradatio…
A Survey on Self-Improving Test-Time Intelligence: Feedback-Driven Adapting, Learning, and Scaling at Inference
Shuaicheng Niu, Guohao Chen, Yaofo Chen +14
The ability of AI systems to improve their behavior during deployment is becoming increasingly important. As inference moves beyond the static execution of a fixed trained model, a…
Preference-Driven Online Adaptation for Personalized Interaction Initiation in Proactive AI Assistants
Yufeng Wang, Wei Zhang, Zhiquan Wen +5
AI assistants are typically reactive, relying on users to initiate interactions. Proactive assistants go beyond this paradigm by autonomously initiating interactions based on users…
Instance-level Visual Active Tracking with Occlusion-Aware Planning
Haowei Sun, Kai Zhou, Hao Gao +5
Visual Active Tracking (VAT) aims to control cameras to follow a target in 3D space, which is critical for applications like drone navigation and security surveillance. However, it…
Beyond Fast and Slow: Cognitive-Inspired Elastic Reasoning for Large Language Models
Jinwu Hu, Dongjin Yang, Langyu Bian +6
Large language models (LLMs) have demonstrated impressive performance across various language tasks. However, existing LLM reasoning strategies mainly rely on the LLM itself with f…
Beyond Model Scaling: Test-Time Intervention for Efficient Deep Reasoning
Qianyue Wang, Jinwu Hu, Yufeng Wang +5
Large Reasoning Models (LRMs) excel at multi-step reasoning but often suffer from inefficient reasoning processes like overthinking and overshoot, where excessive or misdirected re…
EvidFuse: Writing-Time Evidence Learning for Consistent Text-Chart Data Reporting
Huanxiang Lin, Qianyue Wang, Jinwu Hu +3
Data-driven reports communicate decision-relevant insights by tightly interleaving narrative text with charts grounded in underlying tables. However, current LLM-based systems typi…
Continual Knowledge Adaptation for Reinforcement Learning
Jinwu Hu, Zihao Lian, Zhiquan Wen +5
Reinforcement Learning enables agents to learn optimal behaviors through interactions with environments. However, real-world environments are typically non-stationary, requiring ag…
Test-Time Learning for Large Language Models
Jinwu Hu, Zhitian Zhang, Guohao Chen +6
While Large Language Models (LLMs) have exhibited remarkable emergent capabilities through extensive pre-training, they still face critical limitations in generalizing to specializ…
Enhancing User-Oriented Proactivity in Open-Domain Dialogues with Critic Guidance
Yufeng Wang, Jinwu Hu, Ziteng Huang +10
Open-domain dialogue systems aim to generate natural and engaging conversations, providing significant practical value in real applications such as social robotics and personal ass…
Curse of High Dimensionality Issue in Transformer for Long-context Modeling
Shuhai Zhang, Zeng You, Yaofo Chen +5
Transformer-based large language models (LLMs) excel in natural language processing tasks by capturing long-range dependencies through self-attention mechanisms. However, long-cont…
Dynamic Compressing Prompts for Efficient Inference of Large Language Models
Jinwu Hu, Wei Zhang, Yufeng Wang +4
Large Language Models (LLMs) have shown outstanding performance across a variety of tasks, partly due to advanced prompting techniques. However, these techniques often require leng…
Generating Long-form Story Using Dynamic Hierarchical Outlining with Memory-Enhancement
Qianyue Wang, Jinwu Hu, Zhengping Li +4
Long-form story generation task aims to produce coherent and sufficiently lengthy text, essential for applications such as novel writingand interactive storytelling. However, exist…
Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion
Zhuokun Chen, Jinwu Hu, Zeshuai Deng +3
Multimodal LLMs (MLLMs) equip language models with visual capabilities by aligning vision encoders with language models. Existing methods to enhance the visual perception of MLLMs…
Open-World Drone Active Tracking with Goal-Centered Rewards
Haowei Sun, Jinwu Hu, Zhirui Zhang +7
Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations, providing a more practical solution for effe…