16 papers
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…
Guiding LLM Post-training Data Engineering with Model Internals from Sparse Autoencoders
Yi Jing, Zao Dai, Jinwu Hu +4
Model internals encode rich information about how a large language model (LLM) processes its training data; however, post-training data engineering largely relies on external signa…
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…
Training-Free Test-Time Contrastive Learning for Large Language Models
Kaiwen Zheng, Kai Zhou, Jinwu Hu +3
Large language models (LLMs) demonstrate strong reasoning capabilities, but their performance often degrades under distribution shift. Existing test-time adaptation (TTA) methods r…
Precedent-Informed Reasoning: Mitigating Overthinking in Large Reasoning Models via Test-Time Precedent Learning
Qianyue Wang, Jinwu Hu, Huanxiang Lin +5
Reasoning in Large Language Models (LLMs) often suffers from inefficient long chain-of-thought traces with redundant self-exploration and validation, which inflate computational co…
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…