Papers
Research papers from arXiv and related sources
Physics-Constrained Neural Closure for Lattice Boltzmann Large-Eddy Simulation
We present a physics-constrained, data-driven subgrid-scale (SGS) stress closure for large-eddy simulation (LES) in the lattice Boltzmann method (LBM). Trained on filtered-downsampled (FD) data fro...
Muhammad Idrees Khan, Sauro Succi, Hua-Dong Yao, Giacomo Falcucci
Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech
Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies. However, subjective evaluation is costly and difficult to scale, and the scarcity...
Jaesung Bae, Xiuwen Zheng, Minje Kim, Chang D. Yoo, Mark Hasegawa-Johnson
An Agentic Evaluation Framework for AI-Generated Scientific Code in PETSc
While large language models have significantly accelerated scientific code generation, comprehensively evaluating the generated code remains a major challenge. Traditional benchmarks reduce evaluat...
Hong Zhang, Barry Smith, Satish Balay, Le Chen, Murat Keceli, Lois Curfman McInnes, Junchao Zhang
A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Histopathology
Histopathology foundation models (HFMs), pretrained on large-scale cancer datasets, have advanced computational pathology. However, their applicability to non-cancerous chronic kidney disease remai...
Harishwar Reddy Kasireddy, Patricio S. La Rosa, Akshita Gupta, Anindya S. Paul, Jamie L. Fermin, ...
Optimizing Hospital Capacity During Pandemics: A Dual-Component Framework for Strategic Patient Relocation
The COVID-19 pandemic has placed immense strain on hospital systems worldwide, leading to critical capacity challenges. This research proposes a two-part framework to optimize hospital capacity thr...
Sadaf Tabatabaee, Hicham El Baz, Mohammed Khalil Ghali, Nagendra N. Nagarur
POLAR:A Per-User Association Test in Embedding Space
Most intrinsic association probes operate at the word, sentence, or corpus level, obscuring author-level variation. We present POLAR (Per-user On-axis Lexical Association Re-port), a per-user lexic...
Pedro Bento, Arthur Buzelin, Arthur Chagas, Yan Aquino, Victoria Estanislau, Samira Malaquias, Pe...
Evaluating Performance Characteristic of Opportunistic Routing Protocols: A Case Study of the 2016 Italian League Match Earthquake in the Stadio Adriatico
Delay Tolerant Networks (DTNs) can provide emergency communication support when conventional infrastructure is disrupted during disasters. This paper evaluates the performance of opportunistic rout...
Yihang Cao, Milena Radenkovic
Mixture-of-Depths Attention
Scaling depth is a key driver for large language models (LLMs). Yet, as LLMs become deeper, they often suffer from signal degradation: informative features formed in shallow layers are gradually di...
Lianghui Zhu, Yuxin Fang, Bencheng Liao, Shijie Wang, Tianheng Cheng, Zilong Huang, Chen Chen, La...
Look Before Acting: Enhancing Vision Foundation Representations for Vision-Language-Action Models
Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for robotic manipulation, in which reliable action prediction critically depends on accurately interpreting and int...
Yulin Luo, Hao Chen, Zhuangzhe Wu, Bowen Sui, Jiaming Liu, Chenyang Gu, Zhuoyang Liu, Qiuxuan Fen...
HorizonMath: Measuring AI Progress Toward Mathematical Discovery with Automatic Verification
Can AI make progress on important, unsolved mathematical problems? Large language models are now capable of sophisticated mathematical and scientific reasoning, but whether they can perform novel r...
Erik Y. Wang, Sumeet Motwani, James V. Roggeveen, Eliot Hodges, Dulhan Jayalath, Charles London, ...
Mechanistic Origin of Moral Indifference in Language Models
Existing behavioral alignment techniques for Large Language Models (LLMs) often neglect the discrepancy between surface compliance and internal unaligned representations, leaving LLMs vulnerable to...
Lingyu Li, Yan Teng, Yingchun Wang
Tri-Prompting: Video Diffusion with Unified Control over Scene, Subject, and Motion
Recent video diffusion models have made remarkable strides in visual quality, yet precise, fine-grained control remains a key bottleneck that limits practical customizability for content creation. ...
Zhenghong Zhou, Xiaohang Zhan, Zhiqin Chen, Soo Ye Kim, Nanxuan Zhao, Haitian Zheng, Qing Liu, He...
HSImul3R: Physics-in-the-Loop Reconstruction of Simulation-Ready Human-Scene Interactions
We present HSImul3R, a unified framework for simulation-ready 3D reconstruction of human-scene interactions (HSI) from casual captures, including sparse-view images and monocular videos. Existing m...
Yukang Cao, Haozhe Xie, Fangzhou Hong, Long Zhuo, Zhaoxi Chen, Liang Pan, Ziwei Liu
Code-A1: Adversarial Evolving of Code LLM and Test LLM via Reinforcement Learning
Reinforcement learning for code generation relies on verifiable rewards from unit test pass rates. Yet high-quality test suites are scarce, existing datasets offer limited coverage, and static rewa...
Aozhe Wang, Yuchen Yan, Nan Zhou, Zhengxi Lu, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen
Saddle Point Evasion via Curvature-Regularized Gradient Dynamics
Nonconvex optimization underlies many modern machine learning and control tasks, where saddle points pose the dominant obstacle to reliable convergence in high-dimensional settings. Escaping these ...
Liraz Mudrik, Isaac Kaminer, Sean Kragelund, Abram H. Clark
SmartSearch: How Ranking Beats Structure for Conversational Memory Retrieval
Recent conversational memory systems invest heavily in LLM-based structuring at ingestion time and learned retrieval policies at query time. We show that neither is necessary. SmartSearch retrieves...
Jesper Derehag, Carlos Calva, Timmy Ghiurau
AC-Foley: Reference-Audio-Guided Video-to-Audio Synthesis with Acoustic Transfer
Existing video-to-audio (V2A) generation methods predominantly rely on text prompts alongside visual information to synthesize audio. However, two critical bottlenecks persist: semantic granularity...
Pengjun Fang, Yingqing He, Yazhou Xing, Qifeng Chen, Ser-Nam Lim, Harry Yang
OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data
Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet the development of high-performance search agents remains dominated by industria...
Yuwen Du, Rui Ye, Shuo Tang, Xinyu Zhu, Yijun Lu, Yuzhu Cai, Siheng Chen
Effective Distillation to Hybrid xLSTM Architectures
There have been numerous attempts to distill quadratic attention-based large language models (LLMs) into sub-quadratic linearized architectures. However, despite extensive research, such distilled ...
Lukas Hauzenberger, Niklas Schmidinger, Thomas Schmied, Anamaria-Roberta Hartl, David Stap, Piete...
LEXI: Lossless Exponent Coding for Efficient Inter-Chiplet Communication in Hybrid LLMs
Data movement overheads increase the inference latency of state-of-the-art large language models (LLMs). These models commonly use the bfloat16 (BF16) format for stable training. Floating-point sta...
Miao Sun, Alish Kanani, Kaushik Shroff, Umit Ogras