Quantization, pruning, speculative decoding, KV cache, and fast LLM serving.
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…
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…
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…
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…
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…
Dario Compagno, Fabio Massimo Zennaro
Structural causal models (SCMs) were conceived to formulate and answer causal questions. This paper shows that SCMs can also be used to formulate and answer teleological questions, concerning the inte…
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…
A. A. Saoulis, T. -S. Pham, A. M. G. Ferreira
Bayesian inference represents a principled way to incorporate Earth structure uncertainty in full-waveform moment tensor inversions, but traditional approaches generally require significant approximat…
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…
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. …
Skyler Seto, Pierre Ablin, Anastasiia Filippova et al.
Language models achieve impressive performance on a variety of knowledge, language, and reasoning tasks due to the scale and diversity of pretraining data available. The standard training recipe is a …
Xinghao Zhao
Chain-of-thought (CoT) reasoning improves LLM accuracy, yet detecting failures cheaply remains elusive. We study whether the shape of uncertainty dynamics across reasoning steps--captured by sampling …
Chen Zhang, Liwei Liu, Jun Tao et al.
Scientific time series are central to scientific AI but are typically sparse, highly heterogeneous, and limited in scale, making unified representation learning particularly challenging. Meanwhile, fo…
Hao Wang, Licheng Pan, Zhichao Chen et al.
Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models, current reward modeling heavily relies on experimental feedback data collected from human annotato…
Shenggui Li, Chao Wang, Yikai Zhu et al.
Large language models incur high inference latency due to sequential autoregressive decoding. Speculative decoding alleviates this bottleneck by using a lightweight draft model to propose multiple tok…
Huichi Zhou, Siyuan Guo, Anjie Liu et al.
We introduce \emph{Memento-Skills}, a generalist, continually-learnable LLM agent system that functions as an \emph{agent-designing agent}: it autonomously constructs, adapts, and improves task-specif…
Zhicong Lu, Zichuan Lin, Wei Jia et al.
While large language models excel in diverse domains, their performance on complex longhorizon agentic decision-making tasks remains limited. Most existing methods concentrate on designing effective r…
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