4157 papers
The paper introduces LOCATE, a data-light GeoAI framework that optimizes pre-deployment air-quality sensor placement in data-constrained cities by prioritizing population proximity and urban context, demonstrating that a context-augmented population-weighted p-median heuristic outperforms other methods across multiple African cities.
This paper demonstrates that machine learning security detectors, which typically achieve near-perfect performance on standard random splits, suffer catastrophic failure rates when evaluated against unseen attack techniques and temporal shifts using a rigorous, leakage-free protocol across AI prompt injection, malicious package, and network intrusion detection scenarios.
This paper presents a fully reproducible diagnostic demonstrating that in sparse-reward cooperative games, standard additive and pairwise reward projections can achieve low mean squared error while failing catastrophically to identify the optimal joint action, thereby exposing the inadequacy of reconstruction error as a measure of decision quality.
This paper demonstrates that while environment-agnostic agents struggle to solve ARC-AGI-3 tasks from heavily warped and noisy images, the GPT-6-astra model achieves near-ceiling performance under these extreme conditions, significantly outperforming GPT-5.6-sol by effectively combining image processing with decision-making under uncertainty.
This paper presents a numerical audit of Fused LEMNA regression on EMBER features, revealing that while the problem's large null space and strict total-variation constraints cause standard solvers to fail convergence checks, alternative methods like OSQP can achieve lower objectives, though no definitive cause for the failures or security attacks were established.
This study evaluates the trade-off between predictive performance and computational efficiency of ResNet-50, MobileNetV2, and EfficientNet B0 across internal and external plant disease datasets, demonstrating that while ResNet-50 achieves the highest accuracy, MobileNetV2 offers superior throughput and lower latency, suggesting that model selection should balance both metrics rather than relying solely on accuracy rankings.
This paper presents "Global Food Waste Challenge," an immersive VR application developed with Android Studio and Kotlin that utilizes motion-based controls, gaze interaction, and storytelling to educate users on Sustainable Development Goal 12 by simulating realistic food management scenarios and donation processes with high accuracy and engagement.
This paper proposes a configurable, data-driven routing architecture that integrates Random-Key Optimizers with closed-loop probability updates to solve the dynamic probabilistic Open Vehicle Routing Problem, effectively balancing detection probability and resource minimization for UAV search and rescue missions while demonstrating robustness against noisy but not biased priors.
The paper introduces RADAR, a lesion-grounded framework for chest radiograph diagnosis that enforces causal dependence on pathology by training models to rely on specific lesion maps rather than spurious shortcuts, thereby achieving superior localization accuracy and significantly reducing false positive predictions when lesions are synthetically removed compared to conventional classifiers.
This paper introduces the Agent Security Contract (ASC), a deny-by-default authorization framework for tool-using LLM agents that enforces pre-execution gates via policy-constrained tool allowlists and integrity-checked evidence, demonstrating through static trace evaluations that updated policy versions effectively block adversarial attacks while minimizing false positives compared to earlier iterations.