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A new arXiv preprint reports that several physical-AI benchmarks measure overlapping information, potentially changing how researchers rank models and choose evaluation suites.
A new preprint reports that medical vision-language models can appear reliable on familiar data while failing cross-dataset transfer, multimodal alignment and shortcut tests.
A new arXiv preprint proposes ProViP, a training-free method that progressively removes redundant visual tokens and focuses pruning on the attention heads most useful for selecting critical visual information. In one reported LLaVA-1.5-7B experiment, it retained 95.9% of the original performance while delivering a…
An audit of 15 frozen hematology, pathology and general-vision foundation models reports steep drops in cross-dataset accuracy and confidence calibration when white-blood-cell images come from different acquisition conditions.
A new preprint introduces LiDAR-SAM2, which transfers video segmentation from SAM2 to temporally consistent 4D LiDAR labeling using multi-view projection and spatio-temporal aggregation.
A new preprint describes SHIFT-LLM, a post-pruning correction method that uses lightweight linear adapters to approximate the computations removed from large language models. The authors report accuracy gains of up to 15.7 points on Llama-3.1-8B-Instruct across seven zero-shot benchmarks.
A new paper introduces YOLOEZ, an open-source graphical tool that combines image labeling, YOLO model training and defect inference in one no-code workflow for structural inspection.
A new arXiv preprint reports a compact monocular-vision pipeline that identifies sidewalk paths and plans routes on CPU hardware. In the authors’ tests, its SegFormer-B0 model reached a hand-annotated intersection-over-union score of 0.946 at 11.7 milliseconds per frame, while image-space midpoint planning produced…
An arXiv paper introduces PointRL, a reinforcement-learning method that uses hidden annotation evidence to train vision-language models to point more reliably at targets.
A new preprint introduces VGA-BenchV2, a benchmark built from 1,016 prompts, more than 60,000 videos and 36,000 human annotations across 12 video-generation models. Its evaluators are also designed to guide reinforcement-learning fine-tuning.
A new arXiv study finds that AI weld-seam segmentation is highly sensitive to image-acquisition conditions. Polarimetric imaging and transformer architectures performed more reliably than conventional CNNs when welds appeared from previously unseen viewpoints.
A revised arXiv paper proposes a learning framework for deciding which LLM APIs to query and which generated answer to deploy when API costs vary and only the selected answer’s downstream reward is observed.
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