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VoxelSynth3D provides case-consistent within-distribution tissue-error reduction with a localized structural tradeoff, and presents VoxelSynth3D, a training-free 3D image-domain framework for reconstructed CT.
Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to new users under limited target-user data and constrained compute. However, held-out-subject performance can be overstated when test-subject information influences preproce...
Biomedical large language model (LLM) evaluation requires auditable assessment of narrow, evolving, source-grounded subspecialty knowledge. Multiple sclerosis MRI (MS-MRI) provides a high-stakes textual-knowledge test case because correct reasoning requires current diagnostic criteria, standardized acquisition and repo...
Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to new users under limited target-user data and constrained compute. However, held-out-subject performance can be overstated when test-subject information influences preproce...
EEG-Fusion is presented, a failure-informed decision-level fusion framework that treats source-free MI decoding as label-free reliability estimation over heterogeneous experts and suggests that label-free reliability estimation can reduce subject-level failure modes in source-free MI-EEG deployment.
Results show that direct robustness, adversarial transfer, and deployment efficiency are distinct properties of compressed EEG decoders.
Post-training quantization of vision--language models (VLMs) is typically assessed through aggregate task accuracy and memory savings, but preserving a headline score does not guarantee preservation of visual grounding behavior. We present GHOST-Q, a cross-precision controlled evaluation of three 8B VLM families under...
This paper proposes BRUCE (Benchmarking Robustness Under Corruption Escalation), a multimodal reasoning fragility framework for scientific vision-language reasoning that evaluates VLMs'robustness by applying perturbations and distortions to the input images, such as blur or low contrast.
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