Researchers introduced the first foundation model-orchestrated workflow for crash safety design, enabling surrogate-assisted pedestrian protection exploration that reduces evaluation time from hours to seconds. The workf…
Researchers propose a three-layer architecture for verbal reinforcement learning in LLM agents, addressing the retention-forgetting dilemma in non-stationary environments. The system uses rules, evidence, and skills with…
Researchers introduced PuMVR, a benchmark of 1,000 image-text pairs across Punjabi's three scripts, and found that state-of-the-art vision-language models show a systematic Script Gap, with accuracy differences up to 16%…
Researchers proposed a BiSpectral Mamba framework combining multi-scale CNN, spectral attention, bidirectional state-space modeling, and quantum-inspired learning for hyperspectral crop field analysis, achieving 84.83% a…
Researchers introduced Edge-TSR, a continuous edge inference system for roadside perception on the NVIDIA Jetson Orin Nano, finding that benchmark-centric evaluation overstates performance by 20-30% compared to real-worl…
Researchers have developed the first vision transformer-based method for mapping coastal algal blooms using Landsat-8/9 and Sentinel-2 satellite imagery. The deep learning approach outperformed traditional spectral-index…
Researchers introduced GeoDisaster, a benchmark for operational geospatial disaster reasoning with 2,921 instances across 43 question types, and proposed an orchestrated multi-agent framework with 18 disaster-oriented to…
Researchers introduced REINS, a training-free method that steers video diffusion models away from unsafe content at inference time by manipulating internal representations. The approach, which adds a safety direction to …
Researchers introduced a reinforcement learning framework that trains large language models to decouple perception from reasoning by operating over digital twin representations for surgical video question answering, achi…
Researchers from multiple institutions introduced Pareto LoRA, a method that mitigates modality imbalance in unified multimodal models by treating instruction tuning as a bi-objective optimization problem. The approach b…
Researchers propose OR3, a text-to-video retrieval method for operating room clips that converts videos into action-driven digital twins and uses an LLM to generate hypothetical queries for intra-modal matching. The meth…
Researchers propose SierpinskiCam, a method for video retaking that uses Sierpinski triangle pattern cues to improve camera controllability and geometric consistency under large viewpoint changes. The approach augments g…
Researchers propose FATE, a unified framework for event-based object detection that uses Pillar Encoding to preserve fine-grained temporal structure and Frequency-Aware Training to enable high-frequency inference up to 2…
Researchers propose a unified framework for learning geometry-consistent and domain-robust image representations in monocular endoscopy, combining a synthetic data pipeline with Hierarchy-Aware Geometry-Semantic Adaptati…
Researchers developed the first principled method for unsupervised learning of statistics for a maximum entropy model of visual textures, using diffusion-based training and sampling. Despite using only 512 statistics, th…
Researchers introduced DriveJudge, a driving evaluation agent that combines rule-grounded evaluation with Vision-Language Model (VLM) reasoning to assess autonomous driving in a context-aware and interpretable manner. Dr…
Researchers propose a hybrid framework for intraoperative liver registration that combines biomechanical priors with meta-learned residual corrections, achieving improved accuracy and generalization on deformable phantom…
Researchers at TerraTransfer propose a method for end-to-end autonomous driving that eliminates the need for expert demonstrations by decoupling learning to drive from learning to see. The approach uses self-play in vect…
Researchers at arXiv challenge the common assumption that visual attention correlates with reliability in vision-language models. Their VLM Reliability Probe study across multiple models finds that spatial attention metr…
Researchers propose MemSlides, a hierarchical memory framework for personalized presentation generation that separates long-term user profiles, session-level working memory, and reusable tool memory to enable stable pers…