Exploiting Per-Core Leakage: Electromagnetic Side-Channel Monitoring of Multicore Architectures
研究揭示了多核架构中的电磁泄漏机制，并首次展示了每核泄漏利用方法，提出了一种非侵入式的侧信道监测技术以解决多核系统安全分析不足的问题。
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Diffusion ReRoll: Revisable Denoising for Robotic Sequential Prediction
Existing diffusion-based approaches for robotic sequential prediction rely on unidirectional denoising, which struggles to maintain global consistency in long-horizon tasks. This work proposes a reversible denoising mechanism that incorporates a structured re-noising strategy, selectively re-adding noise to temporally stable local regions during the diffusion process. This enables iterative refinement by integrating cross-temporal contextual information, facilitating mutual correction between early and late segments of the predicted sequence. Implemented within a unified video-action diffusion framework, the method combines context-aware denoising with selective re-noising, achieving up to a 56.5% improvement in average success rate on the OGBench and LIBERO-10 benchmarks. Moreover, it demonstrates enhanced robustness and stronger action-video consistency in out-of-distribution scenarios.
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GARAGE: Characterizing the Automation Boundary in LLM-based Attack Graph Generation
Existing automated tools struggle to effectively process unstructured cyber threat intelligence (CTI) and the unique architectures of automotive systems, hindering vehicle-specific attack graph generation. This work proposes GARAGE, a novel framework that integrates retrieval-augmented generation (RAG) with domain-specific automotive security knowledge. Built upon 12,786 CVE entries and 140 incident reports, GARAGE constructs a knowledge base compliant with STIX 2.1 and Auto-ISAC ATM standards, enabling fine-grained kill-chain analysis for tactical-level attack scenario modeling. The approach supports generalization to unseen vehicle architectures and demonstrates accurate knowledge transfer across 320 leave-one-out experiments. Furthermore, it delineates the capability boundaries of large language models in threat analysis and provides cost-performance deployment strategies, thereby enhancing human-machine collaborative TARA processes.
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Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
This work addresses the challenge of achieving high-speed, multi-skilled, perception-driven agile locomotion and smooth gait transitions for quadrupedal robots in complex natural environments. The authors propose the APT-RL framework, which integrates an Action Pre-trained Transformer with reinforcement learning. By leveraging a large-scale 2D motion dataset generated through trajectory optimization, the framework pre-trains a transferable, high-quality motion prior. This prior is then combined with onboard perception and a simplified dynamics model to enable efficient policy learning and deployment on 3D rough terrain. Using only a single policy, the method robustly navigates diverse obstacles—including stairs, steps, and gaps—and achieves a peak speed of 6 m/s, significantly enhancing the robot’s agility and autonomy in both indoor and outdoor complex settings.
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SERF: Spatiotemporal Environment and Robot Feature Map for Long-Horizon Mobile Manipulation
This work addresses the challenge of sustained reasoning about localization, environmental dynamics, and task progress in long-horizon mobile manipulation, where image observations alone are insufficient. The authors propose an online-updatable neural point map that jointly models the environment and robot embodiment as neural points within a shared latent space. By integrating object-level rigid-body tracking with forward kinematics, the method achieves an efficient spatiotemporal representation. The map is dynamically updated using first-person visual observations and proprioceptive states, providing multiscale, multi-view contextual information to vision–language–action policies. Evaluated on the BEHAVIOR-1K benchmark, the approach yields more direct trajectories, faster subgoal completion, and greater robustness to scene changes compared to image-only baselines, and demonstrates the ability to recover from failures such as object drops.
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Exploiting Per-Core Leakage: Electromagnetic Side-Channel Monitoring of Multicore Architectures
研究揭示了多核架构中的电磁泄漏机制，并首次展示了每核泄漏利用方法，提出了一种非侵入式的侧信道监测技术以解决多核系统安全分析不足的问题。
0 citationsRead paper
Diffusion ReRoll: Revisable Denoising for Robotic Sequential Prediction
Existing diffusion-based approaches for robotic sequential prediction rely on unidirectional denoising, which struggles to maintain global consistency in long-horizon tasks. This work proposes a reversible denoising mechanism that incorporates a structured re-noising strategy, selectively re-adding noise to temporally stable local regions during the diffusion process. This enables iterative refinement by integrating cross-temporal contextual information, facilitating mutual correction between early and late segments of the predicted sequence. Implemented within a unified video-action diffusion framework, the method combines context-aware denoising with selective re-noising, achieving up to a 56.5% improvement in average success rate on the OGBench and LIBERO-10 benchmarks. Moreover, it demonstrates enhanced robustness and stronger action-video consistency in out-of-distribution scenarios.
0 citationsRead paper
GARAGE: Characterizing the Automation Boundary in LLM-based Attack Graph Generation
Existing automated tools struggle to effectively process unstructured cyber threat intelligence (CTI) and the unique architectures of automotive systems, hindering vehicle-specific attack graph generation. This work proposes GARAGE, a novel framework that integrates retrieval-augmented generation (RAG) with domain-specific automotive security knowledge. Built upon 12,786 CVE entries and 140 incident reports, GARAGE constructs a knowledge base compliant with STIX 2.1 and Auto-ISAC ATM standards, enabling fine-grained kill-chain analysis for tactical-level attack scenario modeling. The approach supports generalization to unseen vehicle architectures and demonstrates accurate knowledge transfer across 320 leave-one-out experiments. Furthermore, it delineates the capability boundaries of large language models in threat analysis and provides cost-performance deployment strategies, thereby enhancing human-machine collaborative TARA processes.
0 citationsRead paper
Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
This work addresses the challenge of achieving high-speed, multi-skilled, perception-driven agile locomotion and smooth gait transitions for quadrupedal robots in complex natural environments. The authors propose the APT-RL framework, which integrates an Action Pre-trained Transformer with reinforcement learning. By leveraging a large-scale 2D motion dataset generated through trajectory optimization, the framework pre-trains a transferable, high-quality motion prior. This prior is then combined with onboard perception and a simplified dynamics model to enable efficient policy learning and deployment on 3D rough terrain. Using only a single policy, the method robustly navigates diverse obstacles—including stairs, steps, and gaps—and achieves a peak speed of 6 m/s, significantly enhancing the robot’s agility and autonomy in both indoor and outdoor complex settings.
0 citationsRead paper
SERF: Spatiotemporal Environment and Robot Feature Map for Long-Horizon Mobile Manipulation
This work addresses the challenge of sustained reasoning about localization, environmental dynamics, and task progress in long-horizon mobile manipulation, where image observations alone are insufficient. The authors propose an online-updatable neural point map that jointly models the environment and robot embodiment as neural points within a shared latent space. By integrating object-level rigid-body tracking with forward kinematics, the method achieves an efficient spatiotemporal representation. The map is dynamically updated using first-person visual observations and proprioceptive states, providing multiscale, multi-view contextual information to vision–language–action policies. Evaluated on the BEHAVIOR-1K benchmark, the approach yields more direct trajectories, faster subgoal completion, and greater robustness to scene changes compared to image-only baselines, and demonstrates the ability to recover from failures such as object drops.
0 citationsRead paper