Alignment & Safety

Ziyin Zhang, Zihan Liao, Hang Yu et al.

We present F2LLM-v2, a new family of general-purpose, multilingual embedding models in 8 distinct sizes ranging from 80M to 14B. Trained on a newly curated composite of 60 million publicly available h…

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…

Pronob Kumar Barman, Pronoy Kumar Barman

Predictive policing systems that direct patrol resources based on algorithmically generated crime forecasts have been widely deployed across US cities, yet their tendency to encode and amplify racial …

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…

Zehao Li, Zhenyu Wu, Yibo Zhao et al.

Reinforcement Learning (RL) has the potential to improve the robustness of GUI agents in stochastic environments, yet training is highly sensitive to the quality of the reward function. Existing rewar…

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…

Dimitri Kanevsky, Julian Salazar, Matt Harvey

Let $V$ be a smooth cubic surface over a $p$-adic field $k$ with good reduction. Swinnerton-Dyer (1981) proved that $R$-equivalence is trivial on $V(k)$ except perhaps if $V$ is one of three special t…

Qiawen Ella Liu, Marina Dubova, Henry Conklin et al.

Are large language models (LLMs) creative in the same way humans are, and can the same interventions increase creativity in both? We evaluate a promising but largely untested intervention for creativi…

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"…

Xuemian Wu, Shizhe Zhao, Zhongqiang Ren

Multi-Agent Path Finding (MAPF) seeks collision-free paths for multiple agents from their respective start locations to their respective goal locations while minimizing path costs. Most existing MAPF …

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…

Yilin Wang, Yuchun Fan, Jiaoyang Li et al.

Retrieval-augmented generation (RAG) systems have made significant progress in solving complex multi-hop question answering (QA) tasks in the English scenario. However, RAG systems inevitably face the…

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…

Hao Zhang, Mingjie Liu, Shaokun Zhang et al.

Multi-turn LLM agents are increasingly important for solving complex, interactive tasks, and reinforcement learning (RL) is a key ingredient for improving their long-horizon behavior. However, RL trai…

Yogesh Agrawal, Aniruddha Dutta, Md Mahadi Hasan et al.

Real-world financial decision-making is a challenging problem that requires reasoning over heterogeneous signals, including company fundamentals derived from regulatory filings and trading signals com…

Fengxiaoxiao Li, Xiao Mao, Mingfeng Fan et al.

Robotic systems often require a team of robots to collectively visit multiple targets while optimizing competing objectives, such as total travel cost and makespan. This setting can be formulated as t…

Qiawen Ella Liu, Raja Marjieh, Jian-Qiao Zhu et al.

Four-term word analogies (A:B::C:D) are classically modeled geometrically as ''parallelograms,'' yet recent work suggests this model poorly captures how humans produce analogies, with simple local-sim…

Yuyang Liu

Combinatorial optimization problems arise in logistics, scheduling, and resource allocation, yet existing approaches face a fundamental trade-off among generality, performance, and usability. We prese…

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…

Zou Qiang

Large language models (LLMs) demonstrate strong generative capabilities but remain vulnerable to hallucination and unreliable reasoning under adversarial prompting. Existing safety approaches -- such …

Vedanta S P, Ponnurangam Kumaraguru

Large language models are increasingly proposed as autonomous agents for high-stakes public workflows, yet we lack systematic evidence about whether they would follow institutional rules when granted …

Chonghan Liu, Yimin Du, Qi An et al.

Large language models frequently exhibit suboptimal performance on low resource languages, primarily due to inefficient subword segmentation and systemic training data imbalances. In this paper, we pr…

Arthur Dyevre, Ahmad Shahvaroughi

The integration of artificial intelligence (AI) technologies into judicial decision-making - particularly in pretrial, sentencing, and parole contexts - has generated substantial concerns about transp…

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…