arXiv:2511.08360v1 Announce Type: cross Abstract: Deep neural networks (DNNs) are used in many applications, but their large size and high computational cost make them hard to run on devices with limited resources. Two widely used techniques to address this challenge are weight quantization, which lowers the precision of all weights, and structured sparsity, which removes […]
Alignment-Constrained Dynamic Pruning for LLMs: Identifying and Preserving Alignment-Critical Circuits
arXiv:2511.07482v1 Announce Type: cross Abstract: Large Language Models require substantial computational resources for inference, posing deployment challenges. While dynamic pruning offers superior efficiency over static methods through adaptive circuit selection, it exacerbates alignment degradation by retaining only input-dependent safety-critical circuit preservation across diverse inputs. As a result, addressing these heightened alignment vulnerabilities remains critical. We […]
Toward Practical BCI: A Real-time Wireless Imagined Speech EEG Decoding System
arXiv:2511.07936v1 Announce Type: new Abstract: Brain-computer interface (BCI) research, while promising, has largely been confined to static and fixed environments, limiting real-world applicability. To move towards practical BCI, we introduce a real-time wireless imagined speech electroencephalogram (EEG) decoding system designed for flexibility and everyday use. Our framework focuses on practicality, demonstrating extensibility beyond wired EEG […]
Enabling Automatic Self-Talk Detection via Earables
arXiv:2511.07493v1 Announce Type: cross Abstract: Self-talk-an internal dialogue that can occur silently or be spoken aloud-plays a crucial role in emotional regulation, cognitive processing, and motivation, yet has remained largely invisible and unmeasurable in everyday life. In this paper, we present MutterMeter, a mobile system that automatically detects vocalized self-talk from audio captured by earable […]
Anatomy-VLM: A Fine-grained Vision-Language Model for Medical Interpretation
arXiv:2511.08402v1 Announce Type: cross Abstract: Accurate disease interpretation from radiology remains challenging due to imaging heterogeneity. Achieving expert-level diagnostic decisions requires integration of subtle image features with clinical knowledge. Yet major vision-language models (VLMs) treat images as holistic entities and overlook fine-grained image details that are vital for disease diagnosis. Clinicians analyze images by utilizing […]
Clinical Uncertainty Impacts Machine Learning Evaluations
arXiv:2509.22242v2 Announce Type: replace Abstract: Clinical dataset labels are rarely certain as annotators disagree and confidence is not uniform across cases. Typical aggregation procedures, such as majority voting, obscure this variability. In simple experiments on medical imaging benchmarks, accounting for the confidence in binary labels significantly impacts model rankings. We therefore argue that machine-learning evaluations […]
The Path Not Taken: RLVR Provably Learns Off the Principals
arXiv:2511.08567v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) reliably improves the reasoning performance of large language models, yet it appears to modify only a small fraction of parameters. We revisit this paradox and show that sparsity is a surface artifact of a model-conditioned optimization bias: for a fixed pretrained model, updates consistently […]
RAPTR: Radar-based 3D Pose Estimation using Transformer
arXiv:2511.08387v1 Announce Type: cross Abstract: Radar-based indoor 3D human pose estimation typically relied on fine-grained 3D keypoint labels, which are costly to obtain especially in complex indoor settings involving clutter, occlusions, or multiple people. In this paper, we propose textbfRAPTR (RAdar Pose esTimation using tRansformer) under weak supervision, using only 3D BBox and 2D keypoint […]
NERVE: Neighbourhood & Entropy-guided Random-walk for training free open-Vocabulary sEgmentation
arXiv:2511.08248v1 Announce Type: cross Abstract: Despite recent advances in Open-Vocabulary Semantic Segmentation (OVSS), existing training-free methods face several limitations: use of computationally expensive affinity refinement strategies, ineffective fusion of transformer attention maps due to equal weighting or reliance on fixed-size Gaussian kernels to reinforce local spatial smoothness, enforcing isotropic neighborhoods. We propose a strong baseline […]
Beyond the Pixels: VLM-based Evaluation of Identity Preservation in Reference-Guided Synthesis
arXiv:2511.08087v1 Announce Type: cross Abstract: Evaluating identity preservation in generative models remains a critical yet unresolved challenge. Existing metrics rely on global embeddings or coarse VLM prompting, failing to capture fine-grained identity changes and providing limited diagnostic insight. We introduce Beyond the Pixels, a hierarchical evaluation framework that decomposes identity assessment into feature-level transformations. Our […]
FaSDiff: Balancing Perception and Semantics in Face Compression via Stable Diffusion Priors
arXiv:2505.05870v2 Announce Type: replace-cross Abstract: With the increasing deployment of facial image data across a wide range of applications, efficient compression tailored to facial semantics has become critical for both storage and transmission. While recent learning-based face image compression methods have achieved promising results, they often suffer from degraded reconstruction quality at low bit rates. […]
Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attempts
arXiv:2511.07090v2 Announce Type: replace Abstract: Across the Artificial Intelligence (AI) lifecycle – from hardware to development, deployment, and reuse – burdens span energy, carbon, water, and embodied impacts. Cloud provider tools improve transparency but remain heterogeneous and often omit water and value chain effects, limiting comparability and reproducibility. Addressing these multi dimensional burdens requires a […]