16 papers
Training Language Models to Cooperate with Inference-Time Controllers
Moumita Choudhury, Vanshaj Khattar, Jing Liu +4
Large language model (LLM) performance increasingly depends not only on the base model, but also on the inference-time controller used to organize reasoning. Existing post-training…
Beyond Heavy Log Curation: Perplexity-Based APT Detection via Unsupervised, Context-Augmented Language Models
Shoya Otsu, Kei Suzuki, Toshiaki Koike-Akino +2
Advanced Persistent Threats (APTs) remain difficult to detect because only a small fraction of events in large-scale logs are attack-related, and investigation is expensive and har…
VeriSpace: Spatially Grounded Action Verification for Vision-Language-Action Models
Guiyu Zhao, Longteng Guo, Junyou Zhu +6
Vision-language-action (VLA) models have shown strong promise for robotic manipulation, but their reliability at test time remains limited by one-shot action prediction, where even…
ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies
Haodi Hu, Chung-Ta Huang, Jing Liu +4
Vision-language-action (VLA) policies provide strong priors for language-conditioned manipulation, but remain brittle in off-nominal states requiring targeted recovery. We propose…
EinSort: Sorting is All We Need for Tensorizing LLM
Toshiaki Koike-Akino, Jing Liu, Ye Wang
Tensor networks provide efficient representations for compressing large neural networks. By carefully designing shapes and topologies, they can significantly reduce memory and comp…
Diffusion-Guided Semantic Consistency for Multimodal Heterogeneity
Jing Liu, Zhengliang Guo, Yan Wang +4
Federated learning (FL) is severely challenged by non-independent and identically distributed (non-IID) client data, a problem that degrades global model performance, especially in…