12 papers
Few-for-Many Best-Arm Identification in Multi-Objective Bandits
Bo Xue, Yilu Liu, Haodong Jing +3
Real-world systems often serve many objectives but can deploy only a few options, whose performance must be learned from noisy feedback. Modeling each option as an arm in a multi-o…
Is Imagination Derived from Hallucination? A Cross-Taxonomy Evaluation of Imagination and Hallucination in Large Language Models
Zixuan Tang, Hongzong Li, Shuxin Zhuang +2
Imagination performs as a high-level function of large language models (LLMs) which determines the potential of how an LLM creates unseen or creative content. While existing works…
Where World Models Break: Natural-Input Failure Discovery
Zhanpeng Shi, Zi Liang, Rong Feng +3
World models predict action-conditioned futures and serve as critical internal simulators for downstream planning and control. However, catastrophic prediction failures of world mo…
Adversarial Attacks on Deep OCR Systems
Wenbo Sun, Hongzong LI, Yanyun Wang +5
Deep-OCR (DeepSeek-OCR) advances document recognition by treating the visual modality as an optical compression medium, enabling long-context OCR at low token cost. However, its in…
Controlled Memory Interference in Continual LLM Agents
Ao Ding, Hongzong LI, Shiqin Tang +4
Long-term memory enables AI agents to maintain continuity across sessions, personalize behavior, and evolve through accumulated experience. Yet memory evolution is not simply a pro…
EMAN: Optimization-Driven Capacity Growth through Path Emergence in Multi-Task Learning
Chenlei Fang, Jingchen Li, Hongzong LI +5
Existing multi-task learning methods rely on hard sharing, multiple paths or experts, adaptive sharing, and dynamic expansion. However, their capacity changes are usually constrain…
Efficient Online Lexicographic Generalized Low-Rank Matrix Bandits
Bo Xue, Ji Cheng, Haodong Jing +2
This paper studies generalized low-rank matrix bandits with multiple prioritized objectives. At each round, the learner selects a matrix-valued arm and observes a vector-valued rew…
How Vulnerable Are Edge LLMs?
Ao Ding, Hongzong Li, Zi Liang +5
Large language models (LLMs) are increasingly deployed on edge devices under strict computation and quantization constraints, yet their security implications remain unclear. We stu…
How Much Information Can a Vision Token Hold? A Scaling Law for Recognition Limits in VLMs
Shuxin Zhuang, Zi Liang, Runsheng Yu +4
Recent vision-centric approaches have made significant strides in long-context modeling. Represented by DeepSeek-OCR, these models encode rendered text into continuous vision token…
Balancing Efficiency and Fairness: An Iterative Exchange Framework for Multi-UAV Cooperative Path Planning
Hongzong Li, Luwei Liao, Xiangguang Dai +3
Multi-UAV cooperative path planning (MUCPP) is a fundamental problem in multi-agent systems, aiming to generate collision-free trajectories for a team of unmanned aerial vehicles (…
Particle Dynamics for Latent-Variable Energy-Based Models
Shiqin Tang, Shuxin Zhuang, Rong Feng +3
Latent-variable energy-based models (LVEBMs) assign a single normalized energy to joint pairs of observed data and latent variables, offering expressive generative modeling while c…
Adversary-Free Counterfactual Prediction via Information-Regularized Representations
Shiqin Tang, Rong Feng, Shuxin Zhuang +2
We study counterfactual prediction under assignment bias and propose a mathematically grounded, information-theoretic approach that removes treatment-covariate dependence without a…