5 papers
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Ang Li, Ben Liu, Bin Han +215
Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…
MIRAGE: Context-Aware Prompt Injection against Mobile GUI Agents via User-Generated Content
Ruoqi Guo, Yi Liu, Gelei Deng +7
Mobile graphical user interface (GUI) agents driven by vision-language models (VLMs) perceive the screen as rendered pixels and choose actions from what they see, so they cannot re…
Discern Truth from Falsehood: Reducing Over-Refusal via Contrastive Refinement
Yuxiao Lu, Lin Xu, Yang Sun +2
Large language models (LLMs) aligned for safety often suffer from over-refusal, the tendency to reject seemingly toxic or benign prompts by misclassifying them as toxic. This behav…
Semantic Loss Guided Data Efficient Supervised Fine Tuning for Safe Responses in LLMs
Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham
Large Language Models (LLMs) generating unsafe responses to toxic prompts is a significant issue in their applications. While various efforts aim to address this safety concern, pr…
FFConv: Fast Factorized Convolutional Neural Network Inference on Encrypted Data
Yuxiao Lu, Jie Lin, Chao Jin +4
Homomorphic Encryption (HE), allowing computations on encrypted data (ciphertext) without decrypting it first, enables secure but prohibitively slow Convolutional Neural Network (C…