3836 papers
This paper proposes a Distributed Legal Infrastructure (DLI) framework comprising five interlocking layers—ranging from soulbound agent identities to decentralized adjudication—to establish interoperable protocols that ensure accountability, contestability, and rule-of-law principles within the emerging autonomous agentic web.
This paper introduces OPTED, an open-source preprocessed trachoma eye dataset derived from 2,832 images using a zero-shot SAM 3 pipeline to automatically extract and standardize regions of interest, thereby addressing the scarcity of high-quality data for automated trachoma classification in Sub-Saharan Africa.
The paper introduces VertiAdaptor, an online adaptation framework that integrates elevation and semantic data to enable rapid, accurate kinodynamic modeling and planning for autonomous vehicles navigating complex off-road terrains, demonstrating significant improvements in prediction accuracy and adaptation speed in both simulation and real-world tests.
This study demonstrates that a hybrid machine learning model combining Bi-LSTM and GRU algorithms, trained on diverse patient data, achieves a 97.07% accuracy in detecting Coronary Artery Disease, significantly outperforming traditional diagnostic methods.
This paper presents a comprehensive systematization of knowledge on Self-Sovereign Digital Identities (SSDI) by analyzing 80 sources to identify six major adoption challenges, evaluating 47 academic publications and 12 real-world applications to reveal that self-sovereignty is a spectrum, and outlining future research directions to accelerate the shift from centralized to self-sovereign identity systems.
This paper introduces CoPCS, a learning-based framework that utilizes a heterogeneous graph transformer and a synchronized transformer decoder to enable energy- and terrain-constrained UAV-UGV teams to perform coordinated, concurrent task planning and in-mission recharging, significantly improving overall team performance in complex real-world scenarios.
This paper introduces SELSM, a training-free framework that enhances locally deployable medical agents by distilling simulated clinical trajectories into entity-agnostic logical rules, thereby significantly improving zero-shot FHIR-based task performance and achieving a 100% completion rate on the MedAgentBench without compromising data privacy.
SysNav is a novel three-level system that integrates Vision-Language Models with hierarchical planning and embodiment-specific control to achieve robust, efficient, and state-of-the-art real-world object navigation across diverse robot platforms.
PaQ-DETR is a unified object detection framework that addresses query utilization imbalance by dynamically generating image-specific queries from shared latent patterns and employing a quality-aware one-to-many assignment strategy, resulting in consistent mAP improvements across various DETR backbones.
T2Nav is a zero-shot visual navigation system that leverages algebraic topology-aware temporal graph memory and loop detection to integrate heterogeneous data for robust obstacle avoidance, efficient path planning, and reliable navigation to visually similar yet spatially distinct targets in unknown environments.
This paper introduces SurgSync, a time-synchronized multi-modal data collection framework implemented on a da Vinci Research Kit that addresses the lack of training data for surgical AI by integrating advanced sensors and synchronization tools to generate a validated dataset of 214 surgical instances for skill assessment and autonomous control development.
The paper proposes DLRMamba, a novel framework for edge-based multispectral object detection that combines a Low-Rank SS2D module to reduce parameter redundancy with a Structure-Aware Distillation strategy to preserve feature fidelity, achieving superior efficiency and accuracy on resource-constrained hardware.
This paper introduces Load-Aware Informative Path Planning (LIPP), a generalization of classical Informative Path Planning formulated as a Mixed-Integer Quadratic Program that optimizes routing, visitation order, and sampling counts under an energy budget by explicitly modeling the coupling between information gain and load-dependent traversal costs, thereby achieving superior energy efficiency and data collection compared to traditional methods in physical sampling scenarios.
This paper introduces ESM-YOLO+, a lightweight visible-infrared fusion network that employs a Mask-Enhanced Attention Fusion module and training-time Structural Representation enhancement to achieve high-precision small-target detection in complex remote sensing scenes while significantly reducing model complexity compared to baselines.
MindfulAgents is a large language model-driven multi-agent system that personalizes mindfulness meditation through expert-aligned script generation and real-time adaptation, significantly improving user engagement, self-awareness, and stress reduction in both short-term and long-term studies.
This paper proposes a contrastive few-shot RGB-D segmentation framework that integrates sparse 1D laser depth with a negative contrastive learning branch to significantly improve indoor traversability detection and obstacle avoidance compared to existing methods.
This paper presents a physics-informed model derived from hexapedal robot experiments on granular slopes that identifies delayed anchoring and backward slip as primary failure mechanisms, enabling the construction of phase diagrams for quantitative risk estimation of locomotion on deformable inclines.
This paper presents LLM2SMT, a case study demonstrating that an LLM coding agent can autonomously build a complete, competitive DPLL(T)-style SMT solver for QF_UF—including the Nieuwenhuis-Oliveras congruence closure algorithm and Lean proof emission—without any human-written code.
This paper proposes HIERAMP, a method that leverages the coarse-to-fine generation capability of Vision Autoregressive (VAR) models to amplify hierarchical semantics through dynamic class token injection, thereby improving dataset distillation performance by better capturing object structures and details without explicitly optimizing global proximity.
This paper proposes an Adaptive Gain Nonlinear Observer (AGNO) that leverages a full nonlinear dynamic model to accurately and robustly estimate external interaction wrenches in human-UAV physical payload transportation without dedicated force-torque sensors, demonstrating superior performance over Extended Kalman Filters through rigorous stability analysis and simulation.