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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…