5147 papers
The paper proposes UnfoldLDM, a deep unfolding framework that integrates a multi-granularity degradation-aware module for robust degradation estimation and a degradation-resistant latent diffusion model with an over-smoothing correction transformer to effectively address blind image restoration by overcoming degradation-specific dependencies and suppressing over-smoothing bias.
This study analyzes Reddit discourse to reveal how users collectively negotiate ChatGPT privacy risks through practices of risk signaling, norm-setting, and advocacy for privacy-preserving alternatives, offering insights for AI design and privacy literacy.
This paper presents a stable, real-time multi-drone GNSS tracking system for marine robots that integrates visual detection, multi-object tracking, triangulation, and a confidence-weighted Extended Kalman Filter with cross-drone ID alignment to overcome the limitations of underwater signal loss and traditional alternatives.
This paper introduces Yo'City, an agentic framework that leverages large models for hierarchical planning and a self-critic expansion loop to generate personalized, boundless, and spatially coherent 3D realistic city scenes, outperforming existing state-of-the-art methods across multiple evaluation metrics.
DOPD is a dynamic LLM inference system that optimizes goodput and meets strict SLOs by adaptively adjusting the ratio of prefill to decoding instances based on real-time workload monitoring, outperforming existing aggregation and disaggregation approaches like vLLM and DistServe.
This paper initiates the study of fault-tolerant spanners in hypergraphs by demonstrating that simple extensions fail to achieve sublinear size, and subsequently proposes a novel clustering-based algorithm that constructs edge fault-tolerant hyperspanners with improved sublinear size bounds alongside a matching lower bound.
This paper presents an LLM-assisted multi-agent framework that automates the design, tuning, and adaptation of control systems for roll-to-roll manufacturing, significantly reducing manual effort while ensuring safety and maintaining high-quality tension and velocity regulation under model uncertainty.
This paper introduces RadDiff, a novel retrieval-augmented denoising diffusion model that effectively integrates up-to-date protein knowledge into the inverse folding process, significantly outperforming existing methods in sequence recovery rate and foldability while offering greater parameter efficiency and scalability.
This paper proposes a prescriptive maintenance framework that integrates a pre-trained causal foundation model as a "what-if" simulator to identify root causes and recommend optimal interventions, thereby overcoming the limitations of purely predictive models to enhance production-line Overall Equipment Effectiveness (OEE).
The paper proposes S2AM3D, a novel framework that integrates 2D segmentation priors with 3D consistent supervision and a scale-aware prompt decoder to achieve robust, generalizable, and real-time controllable part segmentation for 3D point clouds, supported by a newly introduced large-scale dataset.
This paper presents CAEC, a system built on Arm Confidential Compute Architecture that introduces Confidential Shared Memory to enable secure, high-performance, and attestable direct data sharing between Confidential Virtual Machines while remaining inaccessible to the hypervisor.
HiconAgent introduces History Context-aware Policy Optimization (HCPO), featuring Dynamic Context Sampling and Anchor-guided History Compression, to enable a compact 3B-parameter GUI agent to outperform larger models in navigation accuracy while significantly reducing computational costs.
MAViD is a novel multimodal framework that employs a Conductor-Creator architecture, combining autoregressive audio and diffusion-based video generation with a specialized fusion module, to overcome existing limitations and achieve seamless, long-duration, and contextually coherent audio-visual dialogue understanding and generation.
This paper reveals that visual token information in Vision Large Language Models progressively vanishes at a depth-dependent "information horizon," beyond which existing pruning methods underperform random selection, leading to a novel strategy that integrates random pruning to achieve state-of-the-art efficiency without sacrificing accuracy.
This study demonstrates that in heterogeneous multi-agent settings, a standard IPPO baseline trained via self-play generalizes effectively to novel teammate algorithms, achieving performance comparable to more complex training methods like Rotating Policy Training that explicitly expose agents to diverse partner strategies.
This paper addresses the challenges of off-road road network extraction by introducing the WildRoad dataset and MaGRoad, a novel path-centric framework that overcomes the limitations of existing node-centric models to achieve state-of-the-art performance and faster inference in wild terrains.
The paper introduces ReMeDI-SAM3, a training-free extension of SAM3 that enhances surgical instrument segmentation in endoscopy by implementing relevance-aware memory filtering, piecewise interpolation, and feature-based re-identification to overcome challenges like occlusions and rapid motion, achieving significant zero-shot performance improvements over existing methods.
This study analyzes the discrepancies between objective and subjective urban greenery perceptions across five countries using street view imagery and a survey of 1,000 participants, revealing that while demographics and personality have little influence, an individual's geographic location is a primary factor shaping how they perceive green spaces.
This paper introduces VOIC, a novel dual-decoder framework for monocular 3D Semantic Scene Completion that employs a Visible Region Label Extraction strategy to decouple visible-region perception from occluded-region reasoning, thereby mitigating feature dilution and achieving state-of-the-art performance on standard benchmarks.
This paper demonstrates that reasoning Large Language Models significantly reduce cloud query execution costs and data consumption compared to non-reasoning models in Text-to-SQL tasks, while revealing that execution time is a poor proxy for cost efficiency and highlighting the substantial financial risks posed by non-reasoning models' tendency to generate inefficient queries.