Proposed MCR-Bionic hand integrates anatomical structural priors with hydraulic artificial muscles, achieving dexterous manipulation via wrist-finger coupling and intrinsic muscle pathways.

Haosen Yang, Guowu Wei

EWSegNet combines spatial and spectral features for efficient waste segmentation in cluttered backgrounds, achieving high accuracy with low computational cost.

Mamoona Javaid, Mubashir Noman, Abdul Hannan et al.

CQC-RAG introduces cross-query consistency to enhance robustness in retrieval-augmented generation, outperforming baselines by +4.76 EM on TriviaQA and +9.12 EM on MuSiQue.

Yanjia Sun, Sifan Liu, Jie Shao

SupraSNN employs a superscalar-inspired architecture with synapse-level parallelism, achieving 149μs latency and 0.025mJ/image on FPGA for MNIST, outperforming prior accelerators.

Seyed Sadra Ghavami, Mohammad Hossein Nikkhah, Mohammad Rasoul Roshanshah et al.

ProtoX-AD is a prototype-based self-explainable time series anomaly detection framework that achieves comparable performance to black-box models by leveraging transformation-aware latent representations.

Aitor Sánchez-Ferrera, Elisabeth Wetzer, Kristoffer Wickstrøm et al.

This paper introduces DIRECT, a multimodal scene-aware routing framework that dynamically allocates test-time compute among embodied planners, reducing latency by up to 65% while maintaining or surpassing top performance.

Jadelynn Dao, Milan Ganai, Yasmina Abukhadra et al.

VLGA introduces a dense 3D geometry expert supervised by LiDAR pointmap reconstruction, achieving state-of-the-art safety and driving scores in autonomous driving benchmarks.

Jin Yao, Dhruva Dixith Kurra, Tom Lampo et al.

Proposes DAR-Net, a transformer-based framework with pixel-level scene supervision, achieving 73.33% accuracy in underwater diver activity recognition on the first UDA dataset.

Sadman Sakib Enan, Junaed Sattar

Proposes Turbo-Inference, an iterative inference strategy leveraging detection-segmentation feedback, improving COCO and Cityscapes mAP by over 1% without retraining.

Zhen Zhao, Gang Zhang, Xiaolin Hu et al.

Proposes Bebop with TV loss and rejection sampling to stabilize MTP acceptance rate, achieving up to 95% and 1.8× RL training acceleration.

Yucheng Li, Huiqiang Jiang, Yang Xu et al.

This study compares xLSTM, Mamba-2, and Gated DeltaNet architectures, demonstrating xLSTM's superior performance in complex sequence tasks due to its robust state tracking and memory accumulation.

Anamaria-Roberta Hartl, Levente Zólyomi, David Stap et al.

Proposes Latent World Recovery (LWR), a robust multimodal learning framework that aligns modality-specific embeddings in a shared latent space, handling missing modalities without imputation.

Hui Wang, Tianyu Ren, Joseph Butler et al.

A Unity-based virtual sea trial framework with traceable command-actuation logging supports IMO/ITTC-standard maneuvering data collection for USV system identification.

Paria Rezayan

Proposes a data intermediary model with two-part tariffs to address market failures in AI training data, balancing incentives and model performance.

Yan Dai, Maryam Farboodi, Negin Golrezaei et al.

RankGuard, a decentralized OLTR framework using user click history for robust model filtering, guarantees convergence and resists poisoning attacks.

Marcel Gregoriadis, Martijn de Vos, Sayan Biswas et al.

This paper introduces a metadata-aware multi-prompt reasoning framework for zero-shot accident understanding, achieving a 15% improvement in harmonic mean score on CVPR benchmark.

Tarandeep Singh, Soumyanetra Pal, Soham Biswas et al.

This paper offers a machine learning perspective on uncertainty in dynamical systems, distinguishing aleatoric and epistemic uncertainties, and analyzing their propagation across tasks.

Yusuf Sale, Christopher Bülte, Felix Czaja et al.

This paper introduces two calibration strategies—post-hoc calibration and in-training adaptation—for Bayesian prediction under label shift, validated through synthetic experiments.

Seungjin Choi

Next Forcing introduces multi-chunk prediction to accelerate training and improve accuracy in high-frame-rate video generation, achieving 94.1% success on RoboTwin.

Gangwei Xu, Qihang Zhang, Jiaming Zhou et al.

AMNet introduces modality-agnostic inference for low-light video enhancement, maintaining high performance even with missing auxiliary modalities, outperforming state-of-the-art methods.

Hangfeng Liang, Yutao Hu, Yanhan Hu et al.