SuGaR employs surface-aligned Gaussian splatting for fast, high-quality mesh extraction, enabling real-time scene editing and rendering.

Antoine Guédon, Vincent Lepetit

Integrating Algorithmic Information Theory with kernel methods, the paper proposes Sparse Kernel Flows based on MDL for data-driven kernel learning.

Boumediene Hamzi, Marcus Hutter, Houman Owhadi

TimeVQVAE-AD uses masked generative modeling and frequency factorization for highly accurate, explainable time series anomaly detection, leveraging normal state sampling for interpretability.

Daesoo Lee, Sara Malacarne, Erlend Aune

GLAD uses multi-scale view alignment and background debiasing to improve unsupervised video domain adaptation with large domain gaps.

Hyogun Lee, Kyungho Bae, Seong Jong Ha et al.

Introduces Looped Transformer architecture to emulate iterative algorithms, achieving comparable performance with less than 10% parameters of standard models.

Liu Yang, Kangwook Lee, Robert Nowak et al.

Proposes synchronized multi-view diffusion for consistent text-guided 3D texturing, improving coherence by sharing latent content during denoising.

Yuxin Liu, Minshan Xie, Hanyuan Liu et al.

Introduces Hierarchical Relation Head and Commonsense Validation, boosting scene graph accuracy by over 10% R@50.

Bowen Jiang, Zhijun Zhuang, Shreyas S. Shivakumar et al.

ATLANTIC integrates heterogeneous graph neural networks into retrieval-augmented language models, improving scientific passage relevance and faithfulness by 15%.

Sai Munikoti, Anurag Acharya, Sridevi Wagle et al.

PF-LRM is a pose-free 3D reconstruction model achieving joint shape and pose prediction in 1.3 seconds using transformer-based token interaction and point cloud supervision.

Peng Wang, Hao Tan, Sai Bi et al.

FinanceBench evaluates LLMs on financial QA; GPT-4-Turbo with retrieval answers only 19%, oracle 85%.

Pranab Islam, Anand Kannappan, Douwe Kiela et al.

AutoStory combines LLM-based layout planning with diffusion models to generate diverse, high-quality storytelling images from minimal input, reducing manual effort.

Wen Wang, Canyu Zhao, Hao Chen et al.

LEO, a multi-modal embodied agent trained via 3D VL alignment and instruction tuning, significantly advances 3D scene understanding and interaction.

Jiangyong Huang, Silong Yong, Xiaojian Ma et al.

Sparse OSNAP with s=O(log⁴d) achieves m=(1+θ)d and constant-distortion OSE guarantees.

Shabarish Chenakkod, Michał Dereziński, Xiaoyu Dong et al.

Proposed AgentMonitor framework achieves 89.4% F1 in safe testing of AutoGPT on the open internet.

Silen Naihin, David Atkinson, Marc Green et al.

Introduces Song Describer Dataset (SDD) with 1106 high-quality audio captions for music-language evaluation, enabling cross-task benchmarking.

Ilaria Manco, Benno Weck, SeungHeon Doh et al.

ARES: Automated framework improves RAG evaluation accuracy, reduces manual annotations.

Jon Saad-Falcon, Omar Khattab, Christopher Potts et al.

Proposes MMC-Instruction dataset and MMCA model, significantly improving chart understanding with 600k data and nine-task benchmark.

Fuxiao Liu, Xiaoyang Wang, Wenlin Yao et al.

Proposes grounding acts as metrics to evaluate dialogue, finds LLMs generate 77.5% fewer grounding acts than humans, and preference tuning reduces these acts further.

Omar Shaikh, Kristina Gligorić, Ashna Khetan et al.

Integrating neural quality metrics (COMET-QE) via data filtering, RL training, and reranking significantly improves NMT performance, with experimental gains of up to 3.2 BLEU points.

Miguel Moura Ramos, Patrick Fernandes, António Farinhas et al.

GRASP benchmark evaluates multimodal LLMs' grounding and physics understanding, revealing poor performance in intuitive physics tests.

Serwan Jassim, Mario Holubar, Annika Richter et al.