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新论文 · v1arXiv:2611.16869

Raysemble: Metric 3D Bone Reconstruction from a Single X-ray

Xiaoyang Li, Xinghao Yan, Yuan Chai

原文摘要

Raysemble reconstructs eight lumbar and pelvic bones from a single X-ray by separating shape and placement. Each 3D query samples regional and global features at its image projection. A shared decoder predicts normalized bone shapes. Predicted depth and camera geometry determine 3D centers and scales for resampling these shapes onto a common grid in millimeters. Evaluation uses 23 held-out patients with 43 CT scans, each generating one simulated anteroposterior radiograph. Without rigid alignment to CT,Raysemble leads 15 evaluated methods, averaging 65.24% Dice, 9.09 mm HD95, and 3.32 mm ASSD over three random seeds. With identical inputs, it improves Dice over VolumeNeRF by 5.86 percentage points.

cs.CV

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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.357ACL Anthology · ACL 2026

Enhancing the Transferability of Jailbreak Attacks on Large Language Models via Exploiting Reparameterization Invariance

Ao Wang China University of Petroleum (East China), Xinghao Yang, Yongshun Gong Shandong University, Wei Liu University of Technology Sydney, Bao-di Liu, Weifeng Liu China University of Petroleum (East China)

原文摘要

Jailbreak attacks serve as a pivotal technique for evaluating the safety alignment of Large language models. Current token-level attacks have shown remarkable efficacy on open-source models by leveraging gradient-based optimization. However, these attacks suffer from poor cross-model transferability, severely limiting their utility on proprietary ones. To address this limitation, we propose Reparameterization Invariance Gradient-based Jailbreak (RIGJ), a natural gradient based framework designed to improve cross-model transferability. Unlike prior token-level methods whose optimization paths are constrained by model-specific Euclidean geometry, RIGJ defines update directions according to differences in output distributions rather than parameter-space distances. Since language models are trained to capture similar dependency structures of natural language, their output distributions share common geometry across architectures, yielding intrinsically model-agnostic optimization trajectories and substantially stronger jailbreak transferability. Extensive experiments demonstrate superior performance, increasing the cross-model Attack Success Rate and Average Harmfulness Score by 14.9 and 1.23, respectively. Our code is provided https://github.com/nohuma/AISafety_transfer_jailbreak_RIGJ_2026.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表