4469 papers
The paper presents pqRPKI, a practical post-quantum RPKI framework that utilizes a multi-layer Merkle Tree Ladder and a redesigned manifest structure to significantly reduce bandwidth and validation costs compared to naive post-quantum signature swaps, while enabling efficient dual-stack deployment with minimal overhead.
This review paper analyzes the dual threats posed by quantum computing and artificial intelligence to current cryptographic systems, concluding that a comprehensive defense requires a dynamic, multi-layered strategy combining post-quantum algorithms, implementation hardening, and cryptographic agility to address the limitations of any single solution.
SurgCUT3R is a novel framework that addresses the challenges of data scarcity and pose drift in monocular endoscopic video reconstruction by leveraging a synthetic data generation pipeline, hybrid supervision, and a hierarchical inference strategy to achieve robust, accurate, and efficient 3D surgical scene understanding.
The paper proposes T2SGrid, a novel framework that reformulates video temporal grounding as a spatial understanding task by arranging video frames into composite grid images via overlapping sliding windows, thereby overcoming the limitations of existing temporal encoding methods and achieving superior performance on standard benchmarks.
This paper introduces VSL-Skin, a novel voxel-based phase-change system that bridges soft and rigid robotics by enabling individually addressable, centimeter-scale stiffness modulation, 30% axial compression, and autonomous self-repair to create programmable virtual joints and variable-stiffness morphologies.
This paper introduces a configuration-driven runtime orchestration framework that dynamically generates execution graphs from configuration at request time, enabling low-latency, dependency-aware parallel data retrieval across distributed microservices and APIs without requiring workflow code redeployment.
This paper proposes a novel approach to image-based shape retrieval that leverages pre-aligned multi-modal encoders and a hard contrastive learning loss to achieve state-of-the-art performance in both zero-shot and supervised settings, eliminating the need for explicit view-based supervision or view synthesis.
This paper proposes a perception-aware multimodal reasoning framework that enhances Vision-Language Models' spatial understanding in monocular driving scenarios by representing objects with Visual Reference Tokens and utilizing a Multimodal Chain-of-Thought dataset, achieving significant performance gains on the SURDS benchmark through standard supervised fine-tuning.
This paper introduces ADAS-TO, the first large-scale naturalistic multimodal dataset of 15,659 ADAS-to-manual takeover events from 327 drivers, which combines kinematic and vision-language analysis to characterize safety-critical scenarios and demonstrate that actionable visual cues often precede takeovers by over three seconds.
This paper introduces a lightweight, probabilistic world model built on a pretrained vision foundation model that generates uncertainty-based runtime monitors to accurately detect anomalous failures in bimanual manipulators, outperforming existing baselines while requiring significantly fewer trainable parameters.
MipSLAM is a novel 3D Gaussian Splatting SLAM framework that achieves high-fidelity anti-aliased rendering and robust pose estimation by integrating an Elliptical Adaptive Anti-aliasing algorithm, a Spectral-Aware Pose Graph Optimization module, and a local frequency-domain perceptual loss to overcome aliasing artifacts and trajectory drift.
AdaGen introduces a general, learnable framework that employs reinforcement learning with an adversarial reward to dynamically adapt step-specific parameters during iterative image synthesis, thereby overcoming the limitations of static, manually-designed schedules and achieving superior performance across diverse generative models with reduced inference costs.
This paper introduces a closed-loop, large-language-model-driven evolutionary framework that automatically reconstructs all seven key components of Adaptive Large Neighborhood Search (ALNS), achieving superior performance on TSPLIB benchmarks and revealing novel design patterns through a MAP-Elites guided process.
TrajPred is a novel framework that enhances surgical instrument-tissue interaction recognition in vision-language models by encoding instrument trajectories to capture temporal motion cues and generating fine-grained visual semantic embeddings, thereby significantly improving performance and vision-text alignment on the CholecT50 benchmark.
This paper proposes and formally evaluates a privacy-preserving, patient-centric identity management framework that ensures secure healthcare access by balancing operational reliability with strong protections against linkability and traceability through anonymous pseudonyms and conditional traceability.
This paper proposes a robust two-stage framework for mobile manipulator path planning that combines dimensionality-reduced graph search with numerical optimization to efficiently generate smooth, kinematically feasible trajectories with sub-millimeter accuracy.
This paper proposes an extended Consent-Based Access Control framework that enhances patient autonomy and system performance by shifting conflict resolution to a pre-commit validation phase, formalizing immutable access invariants, and implementing a context-aware emergency mechanism that balances clinical continuity with strict data privacy.
The paper introduces Mozart, an algorithm-hardware co-design framework that leverages 3.5D wafer-scale chiplet architectures with specialized expert allocation and scheduling strategies to overcome communication and memory bottlenecks in the efficient training of large-scale Mixture-of-Experts (MoE) language models.
This study analyzes how 80 student design teams integrated generative AI into their creative process, revealing that while AI serves as a cognitive accelerator for early-stage tasks like brainstorming, human competencies in agency, domain knowledge, imagination, and taste remain essential for interpreting context, validating outputs, and refining design solutions.
This paper presents OV-DEIM, a real-time end-to-end DETR-style open-vocabulary object detector that combines the DEIMv2 framework with a query supplement strategy and a novel GridSynthetic data augmentation technique to achieve state-of-the-art performance and efficiency, particularly for rare categories.