8465 papers
CoEditor++ is a training-free, cognitively structured framework that leverages a two-stage "what-to-edit" and "how-to-edit" reasoning process with self-reflection to achieve state-of-the-art, visually consistent, and interpretable instruction-based image editing using only open-source components.
This paper introduces "ecosystem trust profiles" as a method for digital ecosystems to autonomously define and advertise trusted credentials, demonstrating how this framework enables cross-ecosystem interoperability while preserving sovereignty, though it reveals that such trust remains fragile without additional external governance mechanisms.
ProFocus is a training-free framework that enhances Vision-and-Language Navigation by unifying proactive perception, which generates targeted visual queries to fill information gaps, and focused reasoning, which utilizes Branch-Diverse Monte Carlo Tree Search to prioritize high-value historical contexts, thereby achieving state-of-the-art zero-shot performance on R2R and REVERIE benchmarks.
This paper introduces SKETCHGAIT, a robust gait recognition framework that leverages a novel label-free "SKETCH" modality to extract dense structural cues from RGB images, demonstrating that combining this edge-based representation with traditional parsing methods significantly outperforms existing silhouette- and parsing-based approaches.
This paper introduces PPCMI-SF, a privacy-preserving collaborative framework that utilizes client-specific latent transforms and server-side mapping to achieve high-accuracy, real-time medical image segmentation across heterogeneous institutions while effectively resisting inversion and membership inference attacks without sharing raw data.
This paper presents a digital twin-driven V2X trajectory prediction framework for urban intersections that employs a novel twin loss function alongside standard MSE to enforce traffic rules, collision avoidance, and motion diversity, thereby significantly reducing safety violations while maintaining high prediction accuracy and real-time performance.
AutoThinkRAG is a complexity-aware framework for image-text interaction that improves document question answering by routing queries based on difficulty and decoupling visual interpretation from logical reasoning to achieve state-of-the-art performance with reduced inference costs.
This paper presents TEGA, a closed-loop assistive teleoperation framework that fuses EMG-based intent inference with visuotactile sensing to deliver real-time vibrotactile feedback via a wearable vest, enabling users with upper limb disabilities to intuitively modulate grasp force and significantly improve manipulation stability.
This paper proposes a novel joint learning framework that enables robot policies to extrapolate to novel conditions by learning inverse tasks from forward demonstrations, achieving accurate zero-shot generalization and outperforming diffusion-based alternatives in complex manipulation scenarios.
This paper introduces "Thinking with Spatial Code," a framework that converts RGB videos into explicit, temporally coherent 3D representations using a specialized spatial encoder and reinforcement learning, enabling large language models to achieve state-of-the-art performance in physical-world visual reasoning on VSI-Bench.
This paper proposes a "Test-then-Punish" framework that sustains cooperation in discounted infinitely repeated games with imperfect monitoring by embedding statistical hypothesis testing into strategic behavior, allowing players to detect deviations and enforce a Folk theorem-type result through either anytime valid sequential tests or batch-based testing.
This paper introduces CBM-Suite, a comprehensive framework that addresses key limitations of Concept Bottleneck Models by proposing an entropy-based metric for concept relevance, a non-linear layer to prevent bypassing the bottleneck, and a distillation strategy to close the accuracy gap with opaque models.
This paper presents and experimentally evaluates space-efficient B-tree variants optimized for memory-constrained flash-based embedded devices, demonstrating that storage-specific adaptations enable efficient on-device indexing and processing for IoT applications.
This paper presents the first large-scale taxonomy of real-world faults in Model Context Protocol (MCP) servers, derived from empirical evidence and validated by a practitioner survey, to identify critical error-prone components and guide the development of more robust and secure AI-enabled software systems.
This paper proposes a general control framework that ensures task-space regulation and tracking for underactuated soft robots with bounded inputs by enforcing a rapidly exponentially stabilizing Control Lyapunov function as a convex inequality constraint, demonstrating superior accuracy and stability compared to baseline methods across various simulation platforms.
This paper introduces the RFM-HRI dataset, a multimodal collection of human-robot interactions in medical crash-cart settings that systematically analyzes user verbal and non-verbal reactions to various communication failures and their preferences for recovery strategies to improve safety-critical HRI systems.
This paper proposes "Hybrid Structured Editing," a novel approach that bridges the gap between tool builders and users by enforcing structural constraints on code to ensure reliable tool integration while simultaneously providing programmers with a familiar and consistent text-based editing interface.
This paper identifies and categorizes the specific pitfalls encountered when porting virtual machines to the CHERI architecture, highlighting how common C language assumptions and VM implementation idioms conflict with CHERI's strict memory safety model, and proposes verified workarounds through a case study of porting CRuby.
This paper evaluates nine state-of-the-art Large Language Models on three new benchmarks (CodeGen, Repair, and Explain) within an OCaml functional programming course, revealing that while top models effectively handle syntax/type corrections and conceptual questions, they solve significantly fewer homework problems in this low-resource language compared to high-resource languages like Python and Java.
This paper argues that current generative AI chatbots' risk-avoidant responses to mental health crises can harm users and proposes shifting toward empowerment-oriented design principles that enable AI to act as a supportive bridge for de-escalation and connection to professional care.