7354 papers
This paper applies thermodynamic and economic principles to reframe AI tokens as physical quantities with measurable costs, establishing a finite global "question budget" to argue that the critical challenge for humanity is not the volume of computable answers but the agency required to determine which questions are worth asking.
This paper introduces SmartBench, the first dataset designed to evaluate LLMs on detecting anomalous device states and behavioral contexts in smart homes, revealing that current state-of-the-art models struggle significantly with this critical task.
The paper introduces HEARTS, a comprehensive benchmark comprising 16 real-world health datasets and 110 tasks across four reasoning capabilities, which reveals that current large language models significantly underperform specialized models in health time series analysis due to struggles with multi-step temporal reasoning and reliance on simple heuristics.
RECAP is a bio-inspired image classification method that couples untrained reservoir dynamics with a self-organizing Hebbian prototype readout to achieve robust, backpropagation-free learning capable of generalizing to corrupted inputs without prior exposure.
SR-TTT addresses the catastrophic recall failures of Test-Time Training (TTT) language models by introducing a loss-gated sparse memory mechanism that dynamically routes highly surprising tokens to an exact-attention residual cache, thereby preserving O(1) memory efficiency while enabling accurate retrieval of critical information.
This paper proposes a trust-aware federated learning framework that utilizes an Adaptive Trust Score Scaling and Filtering mechanism to secure bone healing stage interpretation in e-Health by mitigating the impact of unreliable or adversarial participants while maintaining model integrity and predictive performance.
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.
The paper introduces HURRI-GAN, a novel TimeGAN-based framework that corrects systemic biases in high-resolution hurricane simulation models like ADCIRC, enabling accurate, near real-time storm surge forecasting and bias extrapolation beyond gauge station locations while significantly reducing computational runtime.
This paper introduces Geodesic Gradient Descent (GGD), a generic, learning-rate-free optimization algorithm that approximates local neighborhoods of objective function-induced hypersurfaces using n-dimensional spheres to ensure update trajectories remain on the manifold, achieving significant performance improvements over Adam on both regression and classification tasks.
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 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.
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 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 introduces ERP-RiskBench, a leakage-safe ensemble learning framework for detecting financial risks in ERP systems that combines hybrid risk definitions, rigorous time- and group-aware validation to prevent data leakage, and a stacking gradient boosting model to deliver reproducible, interpretable, and operationally grounded fraud detection results.