7 papers
TokenSwap: Backdoor Attack on the Compositional Understanding of Large Vision-Language Models
Zhifang Zhang, Qiqi Tao, Jiaqi Lv +3
The paper introduces TokenSwap, a stealthy backdoor attack on large vision-language models that swaps key textual tokens to corrupt the model's understanding of object relationship…
Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making
Guangfeng Cai, Kaibing Yang, Shuo He +4
Recent studies on world modeling for Large Language Model (LLM) agents typically formulate the learning objective as next-observation prediction. However, this objective ties super…
Breaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform
Jianlu Shen, Fu Feng, Yucheng Xie +2
Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coup…
A Unified Framework for Knowledge Transfer in Bidirectional Model Scaling
Jianlu Shen, Fu Feng, Jiaze Xu +3
Transferring pre-trained knowledge from a source model to a target model of a different architectural size is a key challenge for flexible and efficient model scaling. However, cur…
iScript: A Domain-Adapted Large Language Model and Benchmark for Physical Design Tcl Script Generation
Ning Xu, Zhaoyang Zhang, Senlin Shu +10
Modern EDA flows rely heavily on Tcl scripting, yet general LLMs perform poorly in this domain due to extreme data scarcity, domain-specific semantics, and the high reliability req…
Harmonizing Generalization and Personalization in Ring-topology Decentralized Federated Learning
Shunxin Guo, Jiaqi Lv, Xin Geng
We introduce Ring-topology Decentralized Federated Learning (RDFL) for distributed model training, aiming to avoid the inherent risks of centralized failure in server-based FL. How…