6338 papers
MoMaStage is a structured vision-language framework that enables robust long-horizon indoor mobile manipulation by guiding task planning through a topology-aware Skill-State Graph and ensuring execution reliability via a closed-loop mechanism that triggers semantic replanning upon detecting physical deviations, all without requiring explicit scene mapping.
This study presents a rectified flow-based AI model that generates realistic post-treatment brain MRIs from pre-radiotherapy priors and dose maps for glioma patients, achieving high structural fidelity and significantly faster inference than diffusion models to support adaptive treatment planning.
This paper proposes DDHF, a novel real-time drone detection framework for event cameras that utilizes Non-uniform Discrete Fourier Transform (NDFT) to analyze per-pixel temporal frequency signatures, achieving superior accuracy and significantly lower latency compared to traditional deep learning methods like YOLO.
AULLM++ is a structural reasoning framework that leverages Large Language Models to enhance micro-expression Action Unit detection by fusing multi-granularity visual features with learned AU correlations through a three-stage evidence construction, structure modeling, and deduction-based prediction process, achieving state-of-the-art performance and superior cross-domain generalization.
This paper proposes the Hierarchical Error-Corrective Graph Framework (HECG) for autonomous agents, which integrates Multi-Dimensional Transferable Strategy (MDTS) for precise candidate selection, Error Matrix Classification (EMC) for structured failure attribution, and Causal-Context Graph Retrieval (CCGR) for enhanced contextual reasoning to improve execution reliability in complex, multi-step tasks.
The paper proposes StructBiHOI, a hierarchical framework that combines a jointVAE for long-term planning, a maniVAE for frame-level refinement, and a Mamba-based diffusion denoiser to achieve stable, physically plausible, and semantically aligned long-horizon bimanual hand-object interaction generation.
This paper presents a novel capability architecture and a corresponding Zephyr-based real-time operating system that achieves comprehensive security for embedded devices by fully disaggregating and isolating all software subsystems and peripherals, thereby eliminating the need for a run-time software Trusted Computing Base (TCB) without requiring hardware modifications.
SPIRAL is a closed-loop framework that enhances controllable long-horizon video generation by integrating a reflective planning process with iterative action world modeling, enabling self-improvement through explicit planning, object-centric decomposition, and feedback-driven refinement.
This paper presents and evaluates a scalable dynamic point cloud streaming system that leverages on-the-fly transcoding, demonstrating how caching and speculative transcoding significantly reduce server loads and improve user Quality of Experience to support a higher number of simultaneous clients.
This paper proposes an AI-driven perception method with hierarchical human intention prediction to enable mobile robot chemists in self-driving laboratories to proactively distinguish between human preparatory actions and transient interactions, thereby overcoming the inefficiencies of passive obstruction detection and streamlining human-robot coordination in shared-access scenarios.
The paper presents CliCooper, a multi-client cooperative Split Learning framework that safeguards data privacy through differential privacy and label obfuscation while ensuring training integrity and model provenance via dynamic chained watermarking, effectively mitigating privacy and ownership attacks without compromising model accuracy.
This paper proposes the AFOP-ML framework, an automatic feature optimization-enabled prototypical network that achieves rapid few-shot tactile recognition of both shapes and materials with high accuracy and robustness against perturbations, effectively addressing the challenges of data scarcity and time-consuming training in robotic applications.
The FoMo dataset presents a comprehensive, multi-season collection of over 64 km of diverse robot navigation data from a boreal forest, featuring significant environmental changes like heavy snow and vegetation growth to challenge and evaluate the robustness of state-of-the-art odometry and SLAM systems.
This paper proposes IMaX, a simple yet effective objective based on the InfoMax principle that maximizes mutual information between learned features and latent labels while mitigating class-balance bias through an -entropic term, thereby significantly improving the performance of state-of-the-art semi-supervised domain generalization methods in long-tailed distribution scenarios.
The paper introduces OLLA, an LLM-driven online aggregation framework that accelerates unstructured text analytics by incrementally processing data and employing semantic stratified sampling to achieve high accuracy with significantly reduced latency compared to full-batch processing.
The paper proposes Alfa, an attentive low-rank filter adaptation method that reweights pre-trained semantic features via singular value decomposition and attention mechanisms to achieve efficient, sample-efficient test-time personalization for cross-domain gaze estimation, outperforming existing methods while demonstrating applicability beyond computer vision.
This paper proposes a Hybrid Evaluation-based Genetic Programming (HE-GP) framework that dynamically switches between exact and approximate evaluation modes within an Online Scheduling Algorithm to efficiently solve the Uncertain Agile Earth Observation Satellite Scheduling Problem, achieving significant computational cost reductions while maintaining superior scheduling performance compared to existing methods.
This report from the ALICE 2026 workshop details the SymBa group's efforts to revive and extend Nils Aall Barricelli's 1953 research on symbiogenesis by replicating his 1D numerical organisms, developing 2D extensions and DNA-norm experiments, and exploring the implications of symbiogenesis for the origins of life, open-ended evolution, and collective intelligence in artificial systems.
This letter proposes and evaluates two operating protocols, mode selection and mode combining, for multi-mode pinching-antenna systems to maximize sum rate in multi-user downlink communications via a jointly optimized PSO-KKT algorithm, demonstrating that mode combining offers superior spectral efficiency while mode selection provides a low-complexity alternative with comparable performance.
The paper proposes R2F, an LLM-free framework for zero-shot open-vocabulary object navigation that repurposes ray frontiers as direction-conditioned semantic hypotheses to achieve competitive performance with real-time execution, eliminating the latency and computational overhead of iterative large-model queries.