3831 papers
The paper introduces ResearchEnvBench, a new benchmark designed to evaluate autonomous agents' ability to synthesize complex execution environments for research code, revealing significant current limitations in dependency resolution and version management.
The paper introduces ViroGym, a comprehensive benchmark comprising extensive deep mutational scanning data and real-world tasks to evaluate protein language models for predicting viral variant effects and guiding rational antigen selection for vaccine development.
ButterflyViT introduces a geometric parameterization method that treats Mixture of Experts as rotations of a shared quantized substrate, achieving a 354 memory reduction for Vision Transformers on edge devices while maintaining accuracy through spatial smoothness regularization.
This paper surveys the landscape of robotic foundation models, identifies eleven key industrial implications to establish a 149-criteria assessment framework, and evaluates 324 models to reveal that current industrial readiness is limited and uneven, necessitating a shift from isolated benchmark successes to systematic integration of safety, real-time performance, and robust system deployment.
This paper introduces XMACNet, an explainable, lightweight CNN that combines self-attention mechanisms with multi-modal fusion of RGB images and vegetation indices to achieve high-accuracy chili disease classification suitable for edge deployment.
This paper introduces EarthBridge, a high-fidelity cross-modal translation framework combining Diffusion Bridge Implicit Models and Contrastive Unpaired Translation to achieve second place in the 4th Multi-modal Aerial View Image Challenge by effectively translating between SAR, EO, and IR aerial imagery.
This paper introduces a general-purpose, gradient-based nested co-design framework that jointly optimizes the aerodynamic shape and motion planner of winged robots using neural surrogate models for complex flow conditions, demonstrating superior performance and efficiency over evolutionary baselines in tasks like perching and short landing.
The paper proposes HiDE, a hierarchical dictionary-based entropy modeling framework for learned image compression that enhances coding efficiency by decomposing external priors into global and local dictionaries with cascaded retrieval and employing a context-aware parameter estimator to achieve significant BD-rate savings over state-of-the-art methods.
This paper presents a highly efficient method for 3D point cloud neighbourhood searching that combines Space-Filling Curves with a linear Octree structure and specialized algorithms, achieving up to 10 faster performance and significant cache miss reductions compared to existing solutions while demonstrating strong parallel scalability.
This paper proposes a novel black-box simulation method that combines RRT-style search, sampling-based MPC, and a stability-guided sampling scheme to efficiently discover diverse, long-horizon robot manipulation strategies without relying on labor-intensive human demonstrations or task-specific guidance.
The paper introduces HybridMimic, a hybrid framework that dynamically modulates a centroidal-model-based controller via a learned policy to predict contact states and velocities, thereby enabling robust, dynamics-aware humanoid motion mimicking that reduces tracking errors by 13% compared to state-of-the-art RL baselines.
This paper presents a multi-layer Sim-to-Real framework that utilizes VR-collected eye-head data to train and evaluate gaze-driven controllers for a powered neck exoskeleton, ultimately demonstrating the necessity of personalized control strategies to effectively assist individuals with dropped head syndrome.
This paper introduces AceMAD, a multi-agent debate framework that overcomes the "Martingale Curse" of standard methods by leveraging asymmetric cognitive potential energy—where truth-holders anticipate collective misconceptions—to transform agent convergence from a random walk into a directed drift toward the correct answer.
This paper presents a hybrid machine learning model that combines VGG19, EfficientNet, and ResNet50 for feature extraction with a Bi-LSTM classifier to achieve a 98.83% accuracy in the early detection of Cerebral Palsy from MRI images, outperforming several individual pre-trained models.
This paper introduces "context specification" as a process to transform diffuse stakeholder perspectives into clear, measurable constructs, thereby bridging the gap between abstract AI evaluation methods and the operational realities necessary for successful, value-driven AI deployment.
This paper argues that in decentralized multi-agent reinforcement learning, the instability of the agent-world boundary caused by peer-policy updates leads to the erosion of invariant decision cores, thereby framing the challenge as a continual learning problem driven by boundary drift rather than exogenous task switches.
This study utilizes AI-assisted curation to compile and analyze a comprehensive database of 23,793 presentations from the Society for Social Work and Research Annual Conference (2005–2026), revealing significant growth in participation, collaboration, and international engagement alongside a continued predominance of quantitative research methods.
This paper introduces NetFI, a novel fault injection tool, to comprehensively analyze how packet loss, delay, and communication loss impact telesurgical task performance and motion primitives across different proficiency levels, providing quantitative insights and open-source resources to define operational boundaries and guide the development of robust network-aware control strategies.
This paper establishes that the quality of a model's step-level visual grounding, quantified by the Step Grounding Rate (SGR), serves as a robust and independent predictor of out-of-distribution generalization in long-horizon vision-language models, outperforming traditional final-answer accuracy metrics.
This paper proposes a receding-horizon, actuation-aware control allocation strategy for fully actuated omnidirectional UAVs that utilizes nullspace optimization and Constrained iterative LQR to anticipate and suppress asymmetric motor-induced oscillations, thereby significantly improving trajectory tracking performance compared to conventional methods.