Diffusion models, image generation, text-to-image synthesis, and generative AI.
Amartya Mukherjee, Maxwell Fitzsimmons, David C. Del Rey Fernández et al.
Uncertainty quantification for partial differential equations is traditionally grounded in discretization theory, where solution error is controlled via mesh/grid refinement. Physics-informed neural n…
Edward Lin, Sahil Modi, Siva Kumar Sastry Hari et al.
As agentic AI systems become increasingly capable of generating and optimizing GPU kernels, progress is constrained by benchmarks that reward speedup over software baselines rather than proximity to h…
Zhuolin Yang, Zihan Liu, Yang Chen et al.
We introduce Nemotron-Cascade 2, an open 30B MoE model with 3B activated parameters that delivers best-in-class reasoning and strong agentic capabilities. Despite its compact size, its mathematical an…
Huaide Jiang, Yash Chaudhary, Yuping Wang et al.
There are two major categories of embodied navigation: Vision-Language Navigation (VLN), where agents navigate by following natural language instructions; and Object-Goal Navigation (OGN), where agent…
Tianjiao Yu, Xinzhuo Li, Muntasir Wahed et al.
Understanding and generating 3D objects as compositions of meaningful parts is fundamental to human perception and reasoning. However, most text-to-3D methods overlook the semantic and functional stru…
Maksym Del, Markus Kängsepp, Marharyta Domnich et al.
Uncertainty estimation is critical for deploying reasoning language models, yet remains poorly understood under extended chain-of-thought reasoning. We study parallel sampling as a fully black-box app…
Swagat Padhan, Lakshya Jain, Bhavya Minesh Shah et al.
Robots collaborating with humans must convert natural language goals into actionable, physically grounded decisions. For example, executing a command such as "go two meters to the right of the fridge"…
Carlos Hinojosa, Clemens Grange, Bernard Ghanem
Vision-language models (VLMs) are increasingly deployed in real-world and embodied settings where safety decisions depend on visual context. However, it remains unclear which visual evidence drives th…
Mohammad Al Ridhawi, Mahtab Haj Ali, Hussein Al Osman
Stock markets exhibit regime-dependent behavior where prediction models optimized for stable conditions often fail during volatile periods. Existing approaches typically treat all market states unifor…
Weilin Chen, Jiahao Rao, Wenhao Wang et al.
The creation of high-fidelity, customizable 3D indoor scene textures remains a significant challenge. While text-driven methods offer flexibility, they lack the precision for fine-grained, instance-le…
Jonathan Lys, Vincent Gripon, Bastien Pasdeloup et al.
Discrete diffusion models are promising alternatives to autoregressive approaches for text generation, yet their decoding methods remain under-studied. Standard decoding methods for autoregressive mod…
Zhan Jin, Yu Luo, Yizhou Zhang et al.
Conventional pixel-wise loss functions fail to enforce topological constraints in coronary vessel segmentation, producing fragmented vascular trees despite high pixel-level accuracy. We present ARIADN…
Hangeol Chang, Changsun Lee, Seungjoon Rho et al.
Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by grounding generation in external, non-parametric knowledge. However, when a task requires choosing among competing options…
Wenxuan Zhang, Lemeng Wu, Changsheng Zhao et al.
Diffusion Large Language Models (dLLMs) introduce a new paradigm for language generation, which in turn presents new challenges for aligning them with human preferences. In this work, we aim to improv…
Rui Chai
We study online resource allocation among N interacting modules over T rounds. Unlike standard online optimization, costs are endogenous: they depend on the full allocation vector through an interacti…
Min Hun Lee
Artificial intelligence (AI) systems are deployed as collaborators in human decision-making. Yet, evaluation practices focus primarily on model accuracy rather than whether human-AI teams are prepared…
Yikai Zheng, Xin Ding, Yifan Yang et al.
Recent advances in Streaming Video Understanding has enabled a new interaction paradigm where models respond proactively to user queries. Current proactive VideoLLMs rely on per-frame triggering decis…
Krzysztof Janowicz, Gengchen Mai, Rui Zhu et al.
Understanding how AI will represent and reason about geography should be a key concern for all of us, as the broader public increasingly interacts with spaces and places through these systems. Similar…
Sophia Tang
At the core of modern generative modeling frameworks, including diffusion models, score-based models, and flow matching, is the task of transforming a simple prior distribution into a complex target d…
An Luo, Jin Du, Xun Xian et al.
Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) and artificial intelligence (AI) a…
Quentin Guimard, Federico Bartsch, Simone Caldarella et al.
Models that bridge vision and language, such as CLIP, are key components of multimodal AI, yet their large-scale, uncurated training data introduce severe social and spurious biases. Existing post-hoc…
Sheng Liu, Panos Papadimitratos
FL has emerged as a transformative paradigm for ITS, notably camera-based Road Condition Classification (RCC). However, by enabling collaboration, FL-based RCC exposes the system to adversarial partic…
Chun-Jui Wang, Jian-Ting Guo, Hung Guei et al.
Tetris Block Puzzle is a single player stochastic puzzle in which a player places blocks on an 8 x 8 grid to complete lines; its popular variants have amassed tens of millions of downloads. Despite th…
Yitong Li, Igor Yakushev, Dennis M. Hedderich et al.
Positron emission tomography (PET) is a widely recognized technique for diagnosing neurodegenerative diseases, offering critical functional insights. However, its high costs and radiation exposure hin…
Youngwan Lee, Soojin Jang, Yoorhim Cho et al.
Spatial reasoning is foundational for Vision-Language Models (VLMs), particularly when deployed as Vision-Language-Action (VLA) agents in physical environments. However, existing benchmarks predominan…
Xiao Feng, Bo Han, Zhanke Zhou et al.
Reinforcement learning (RL) holds significant promise for enhancing the agentic reasoning capabilities of large language models (LLMs) with external environments. However, the inherent sparsity of ter…
Bishoy Galoaa, Shayda Moezzi, Xiangyu Bai et al.
Recent research has made substantial progress on video reasoning, with many models leveraging spatio-temporal evidence chains to strengthen their inference capabilities. At the same time, a growing se…
Gabriele Carrino, Andrea Sassella, Nicolo Brunello et al.
Recent advances in large language models (LLMs) highlight the importance of post training techniques for improving reasoning and mathematical ability. Group Relative Policy Optimization (GRPO) has sho…
Yuchen Li, Amanmeet Garg, Shalini Chaudhuri et al.
Large Vision Language Models (LVLMs) excel at semantic understanding but struggle with fine grained spatial grounding, as the model must implicitly infer complex geometry without ever producing a spat…
Mingyang Liu, Yongshan Chen, Zhiyuan Fan et al.
Online learning in arbitrary, and possibly adversarial, environments has been extensively studied in sequential decision-making, and it is closely connected to equilibrium computation in game theory. …
Sign up free and get daily digests tailored to your research interests.
Sign up free