3822 papers
This paper proposes a stateful, hierarchical multi-agent framework for 5G/6G Intent-Based Networking orchestration that leverages both Large and Small Language Models, demonstrating that while both achieve similar translation accuracy, Small Language Models improve the overall lifecycle completion speed by 20%.
This paper introduces ObjChangeVR, a novel framework and corresponding dataset designed to enhance object state change reasoning in virtual reality by addressing the challenges of detecting background changes without direct interaction through viewpoint-aware retrieval and cross-view reasoning.
This paper proposes a margin-consistent deep subtyping framework for invasive lung adenocarcinoma that integrates attention-weighted aggregation, contrastive regularization, and a novel Perturbation Fidelity scoring mechanism to achieve robust, high-accuracy classification across multiple architectures and demonstrate cross-institutional generalizability on whole-slide images.
PaLMR is a novel framework that enhances the faithfulness of multimodal large language models by aligning both the reasoning process and outcomes through a perception-aligned data layer and a hierarchical reward fusion scheme, thereby significantly reducing visual hallucinations while achieving state-of-the-art performance on key benchmarks.
This paper proposes a Digital Twin-enabled framework (DAPR) that integrates asynchronous federated learning, a GRU-VAE prediction model, and deep reinforcement learning to optimize client selection and content request prediction, thereby significantly improving cache hit ratios and reducing transmission latency in vehicular edge computing systems.
This paper introduces FCBNet, a parameter-efficient convolutional model featuring a frozen ConvNeXt backbone and a Feature Correction Block that achieves superior weed segmentation accuracy (over 85% mIoU) and computational efficiency across RGB and multispectral aerial imagery compared to existing state-of-the-art models.
The paper introduces GameVerse, a comprehensive benchmark featuring a novel reflect-and-retry paradigm and a hierarchical taxonomy across 15 games, demonstrating that Vision-Language Models can effectively improve their gameplay policies through video-based reflection by combining failure trajectories with expert tutorials.
The paper introduces ASMIL, a unified framework that addresses unstable attention dynamics, overfitting, and over-concentrated attention in attention-based multiple instance learning for whole slide imaging by employing an anchor model with a normalized sigmoid function and token random dropping, resulting in significant performance improvements over state-of-the-art methods.
This chapter explores the potential of generative AI to enhance K-16+ science literacy by proposing a coherent architectural framework that aligns the teaching, learning, and assessment of scientific knowledge and reasoning, while addressing associated challenges and outlining future research needs.
The paper proposes Graph-of-Mark (GoM), a novel pixel-level visual prompting technique that overlays scene graphs onto images to capture object relationships, thereby significantly enhancing the spatial reasoning and zero-shot performance of multimodal language models.
This paper introduces a system-level inference optimization for Diffusion Transformer-based video generation that employs a sequence-parallel Causal-RoPE mechanism and operator fusion to overcome memory and latency bottlenecks, achieving near real-time speeds and sub-second first-frame latency on an eight-GPU cluster.
This paper reveals that Chain-of-Thought prompting often underperforms direct answering in medical visual question answering due to a "medical perception bottleneck," and proposes training-free grounding interventions to restore visual accuracy and improve model reasoning.
This paper introduces SJD-PV, a training-free acceleration framework for autoregressive image generation that leverages phrase-level speculative verification based on token co-occurrence statistics to jointly validate multiple correlated tokens, achieving up to 30% faster decoding without compromising visual fidelity.
This paper presents the first demonstration of a low-latency, always-on fully-connected mesh network using four RFSoC-based software-defined radios that achieves an aggregate throughput of 1.2 Gbps to simultaneously stream twelve real-time, uncompressed 4K video feeds over 2x2 MIMO links.
This paper presents SDN-SYN PoW, an adaptive defense architecture that leverages a Software-Defined Networking controller to dynamically adjust non-interactive Proof-of-Work difficulty based on real-time traffic analysis, effectively mitigating multi-domain SYN floods while maintaining negligible overhead for legitimate clients.
This paper introduces SIL-GPO, a reinforcement learning framework that combines graph attention networks with self-imitation learning to optimize the joint deployment and routing of heterogeneous edge AI and microservices, significantly reducing end-to-end latency and improving resource utilization compared to existing methods.
The paper proposes CalibFusion, a Transformer-based differentiable calibration framework that learns to implicitly refine Radar-Camera extrinsics end-to-end to overcome the challenges of textureless, cluttered water-surface environments and significantly improve fusion-based 2D object detection.
This paper investigates whether semantic noise initialization, known to improve image diffusion models, transfers to text-to-video generation, finding that while it shows a slight positive trend on temporal metrics, it does not significantly outperform standard Gaussian noise due to weak or unstable signals in the noise space.
AutoFigure-Edit is an end-to-end system that generates fully editable, high-quality scientific illustrations from long-form text with flexible style adaptation via reference images, leveraging long-context understanding and native SVG support to overcome limitations in editability and efficiency found in existing automated tools.
The paper introduces MultiGen, a novel diffusion-based game engine that incorporates an explicit, persistent external memory to enable user-editable world structures and support coherent, real-time multiplayer interactions, overcoming the limitations of conventional next-frame prediction models.