12 papers
When Efficiency Becomes Fragility: Exploiting Dynamic Routing Vulnerabilities in Adaptive UAV Tracking
Shaofeng Liang, Runwei Guan, Wenshuo Chen +7
Resource constraints on UAV platforms have driven a paradigm shift in aerial tracking, from pursuing performance toward balancing accuracy with efficiency. Adaptive Transformer Tra…
HyBIRD: Hyperbolic Bridge Retrieval and Diagnosis for Methodology Inspiration Retrieval
Yang Yang, Boyun Xu, Hao Fu +6
Methodology Inspiration Retrieval (MIR) asks a system to retrieve prior papers whose methods can inspire a new research proposal. Unlike general scientific retrieval, the central c…
CoSPlay: Cooperative Self-Play at Test-Time with Self-Generated Code and Unit Test
Zhangyi Hu, Chenhui Liu, Tian Huang +6
Recently, Reinforcement Learning with Verifiable Rewards (RLVR) and Test-Time Scaling (TTS) have advanced LLM code generation through executable verification. Yet Ground-Truth Unit…
TurboEvolve: Towards Fast and Robust LLM-Driven Program Evolution
Yang Yang, Zining Zhong, Jindong Li +5
LLM-driven program evolution can discover high-quality programs, but high API cost and run-to-run variance hinder reliable progress. We propose TurboEvolve, a multi-island evolutio…
POLARIS: Projection-Orthogonal Least Squares for Robust and Adaptive Inversion in Diffusion Models
Wenshuo Chen, Haosen Li, Shaofeng Liang +6
The Inversion-Denoising Paradigm, which is based on diffusion models, excels in diverse image editing and restoration tasks. We revisit its mechanism and reveal a critical, overloo…
Text2Weight: Bridging Natural Language and Neural Network Weight Spaces
Bowen Tian, Wenshuo Chen, Zexi Li +3
How far are we really from automatically generating neural networks? While neural network weight generation shows promise, current approaches struggle with generalization to unseen…
IMTS is Worth Time Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction
Zhangyi Hu, Jiemin Wu, Hua Xu +5
Irregular Multivariate Time Series (IMTS) forecasting is challenging due to the unaligned nature of multi-channel signals and the prevalence of extensive missing data. Existing met…
Hypothesis Generation via LLM-Automated Language Bias for ILP
Yang Yang, Jiemin Wu, Yutao Yue
Inductive Logic Programming (ILP) is a principled approach for generalizing regularities from data and constructing hypotheses as interpretable logic programs. However, a key limit…
DRIVE: Dual-Robustness via Information Variability and Entropic Consistency in Source-Free Unsupervised Domain Adaptation
Ruiqiang Xiao, Songning Lai, Yijun Yang +3
Adapting machine learning models to new domains without labeled data, especially when source data is inaccessible, is a critical challenge in applications like medical imaging, aut…
Beyond Task Vectors: Selective Task Arithmetic Based on Importance Metrics
Tian Bowen, Lai Songning, Wu Jiemin +3
Pretrained models have revolutionized deep learning by enabling significant performance improvements across a wide range of tasks, leveraging large-scale, pre-learned knowledge rep…
Maintaining Informative Coherence: Migrating Hallucinations in Large Language Models via Absorbing Markov Chains
Jiemin Wu, Songning Lai, Ruiqiang Xiao +3
Large Language Models (LLMs) are powerful tools for text generation, translation, and summarization, but they often suffer from hallucinations-instances where they fail to maintain…
DRIVE: Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving
Songning Lai, Tianlang Xue, Hongru Xiao +7
Recent advancements in autonomous driving have seen a paradigm shift towards end-to-end learning paradigms, which map sensory inputs directly to driving actions, thereby enhancing…