3956 papers
FrameVGGT addresses the unbounded memory growth in streaming Visual Geometry Transformers by introducing a frame-driven rolling explicit-memory framework that aggregates frame-level evidence into compact prototypes, enabling stable long-sequence 3D perception under strict memory budgets.
This paper introduces RoboPCA, a pose-centered affordance learning framework that jointly predicts task-appropriate contact regions and poses from human demonstrations via the Human2Afford data curation pipeline, enabling robots to effectively manipulate objects with improved consistency and generalization across tasks and categories.
This paper proposes CDA-VSR, a compressed-domain-aware online video super-resolution network that leverages motion vectors, residual maps, and frame types to achieve real-time, high-quality reconstruction with significantly reduced computational cost compared to state-of-the-art methods.
This paper introduces the Masked Motion Diffusion Model (MMDM), a diffusion-based framework equipped with a Kinematic Attention Aggregation mechanism that learns context-adaptive motion priors to effectively reconstruct, refine, and complete 3D human motion from incomplete or noisy data.
C-Explorer is a decentralized framework for multi-UAV exploration that addresses communication limitations and inefficient traversal by utilizing connectivity-aware task representation and a contiguity-driven allocation strategy, achieving significant reductions in exploration time and path length compared to state-of-the-art methods.
TDM-R1 introduces a novel reinforcement learning paradigm that enables few-step diffusion models to effectively incorporate non-differentiable rewards by decoupling surrogate reward learning from generator training, achieving state-of-the-art performance across various metrics and scaling to powerful models like Z-Image with only 4 inference steps.
The paper introduces PARSE, a framework utilizing part-level geometric relations encoded in Part-centric Assembly Graphs to resolve spatial ambiguities, which is validated through the creation of the PARSE-10K dataset and demonstrated to significantly enhance both object layout reasoning in vision-language models and the physical realism of generated 3D scenes.
VoiceSHIELD-Small is a lightweight, real-time model built on Whisper-small that simultaneously transcribes speech and detects malicious content with 99.16% accuracy, offering a faster and more secure alternative to traditional text-based filtering for voice AI systems.
This paper presents YAQIN, a co-designed AI application that integrates Islamic frameworks and user-centered design to provide culturally sensitive mental health support for Muslim women in the UK, addressing gaps in trust and engagement through a faith-aware chatbot and guided journaling tool.
This Systematization of Knowledge (SoK) paper provides a comprehensive historical analysis of Maximal Extractable Value (MEV) by organizing fragmented literature into three chronological eras—from Miner Extractable Value in Proof-of-Work systems to the modern cross-chain frontier—while offering a unified taxonomy, identifying mitigations, and proposing a research agenda for standardized metrics and cross-chain infrastructure.
This paper demonstrates that large language models exhibit robust decision biases in two-arm bandit tasks, characterized by stubborn exploitation and low learning rates that persist across decoding parameters, thereby posing significant challenges for optimal human-AI collaboration.
This study evaluates Markov chain-based trust models for Vehicular Ad Hoc Networks (VANETs) and demonstrates that increasing the granularity of behavioral states from 4 to 11 significantly enhances the system's ability to capture complex driver dynamics and improve security.
This paper proposes VRec, a novel "reason-verify-recommend" paradigm that interleaves reasoning with multi-dimensional verification to mitigate reasoning degradation and enhance the effectiveness and scalability of LLM-based generative recommendation.
This paper proposes and validates a post-quantum secure federated learning framework that integrates NIST-standardized CRYSTALS-Kyber and CRYSTALS-Dilithium algorithms to protect collaborative threat intelligence from quantum attacks, achieving high detection accuracy with minimal latency while ensuring privacy compliance.
This paper introduces the Three Praxes Framework, derived from a grounded theory analysis of 90 social accessibility studies (2011–2025), to map the field's fragmented practices across Artifact, Ecosystem, and Epistemology sites and advocate for an integrated cycle where lived experiences, material realities, and theoretical knowledge collectively transform access ecosystems.
This paper proposes a novel multi-agent architecture that automates structural modeling and analysis using OpenSeesPy by decomposing complex tasks into specialized agents to effectively reduce hallucinations and error accumulation, achieving high accuracy and scalability across benchmark frame problems.
This paper proposes a framework for synthesizing tunable input-to-state safety (TISSf) controllers that explicitly incorporate general compact input constraints by deriving verifiable certificates and design conditions to guarantee input compatibility and recursive feasibility, as demonstrated in a connected cruise control application.
This paper argues that socially assistive technology should shift its goal from fostering independence to achieving "relational sovereignty," a framework that empowers disabled individuals to choose between independence and interdependence by integrating self-determination theory, symbolic interactionism, and crip technoscience into a new design matrix and set of interventions.
This paper introduces AeroPlace-Flow, a training-free framework that enables aerial manipulators to perform precise, language-grounded object placement by synthesizing goal images, grounding them into 3D space, and generating collision-aware object flows without requiring predefined poses or task-specific training.
This paper proposes reframing human-AI-robot interaction as "scaffolding" rather than autonomy, positioning humans as creative directors who define intent and steer revisions while AI mediates robotic execution to support sustained human agency and creativity in open-ended environments.