Papers
Research papers from arXiv and related sources
Proof-of-Authorship for Diffusion-based AI Generated Content
Recent advancements in AI-generated content (AIGC) have introduced new challenges in intellectual property protection and the authentication of generated objects. We focus on scenarios in which an ...
De Zhang Lee, Han Fang, Ee-Chien Chang
Language on Demand, Knowledge at Core: Composing LLMs with Encoder-Decoder Translation Models for Extensible Multilinguality
Large language models (LLMs) exhibit strong general intelligence, yet their multilingual performance remains highly imbalanced. Although LLMs encode substantial cross-lingual knowledge in a unified...
Mengyu Bu, Yang Feng
Interpreting Context-Aware Human Preferences for Multi-Objective Robot Navigation
Robots operating in human-shared environments must not only achieve task-level navigation objectives such as safety and efficiency, but also adapt their behavior to human preferences. However, as h...
Tharun Sethuraman, Subham Agrawal, Nils Dengler, Jorge de Heuvel, Teena Hassan, Maren Bennewitz
QuantFL: Sustainable Federated Learning for Edge IoT via Pre-Trained Model Quantisation
Federated Learning (FL) enables privacy-preserving intelligence on Internet of Things (IoT) devices but incurs a significant carbon footprint due to the high energy cost of frequent uplink transmis...
Charuka Herath, Yogachandran Rahulamathavan, Varuna De Silva, Sangarapillai Lambotharan
Inducing Epistemological Humility in Large Language Models: A Targeted SFT Approach to Reducing Hallucination
Large language models (LLMs) often hallucinate, producing fluent but false information, partly because supervised fine-tuning (SFT) implicitly rewards always responding. We introduce $\textit{HypoT...
Cem Uluoglakci, Tugba Taskaya Temizel
From Optimizable to Interactable: Mixed Digital Twin-Empowered Testing of Vehicle-Infrastructure Cooperation Systems
Sufficient testing under corner cases is critical for the long-term operation of vehicle-infrastructure cooperation systems (VICS). However, existing corner-case generation methods are primarily AI...
Jianghong Dong, Chunying Yang, Mengchi Cai, Chaoyi Chen, Qing Xu, Jianqiang Wang, Keqiang Li
DustNET: enabling machine learning and AI models of dusty plasmas
Dusty plasmas are ubiquitous throughout the universe, spanning laboratory and industrial plasmas, fusion devices, planetary environments, cometary comae, and interstellar media. Despite decades of ...
Zhehui Wang, Justin C. Burton, Niklas Dormagen, Cheng-Ran Du, Yan Feng, John E. Foster, Max Klein...
Learning When to Attend: Conditional Memory Access for Long-Context LLMs
Language models struggle to generalize beyond pretraining context lengths, limiting long-horizon reasoning and retrieval. Continued pretraining on long-context data can help but is expensive due to...
Sakshi Choudhary, Aditya Chattopadhyay, Luca Zancato, Elvis Nunez, Matthew Trager, Wei Xia, Stefa...
Adaptive near-contact repulsion in conservative Allen-Cahn phase-field lattice Boltzmann multiphase model
Unresolved thin-film dynamics often causes spurious coalescence in diffuse-interface simulations of multiphase flows. We address this issue by introducing a fully local repulsive near-contact flux ...
Andrea Montessori, Maria Rosa Lisboa, Marco Lauricella, Sauro Succi
A New Fractional Step Structure Preserving Method for The Landau-Lifshitz-Gilbert Equation
In this paper, we propose a structure preserving method using a Crank-Nicolson's type method with an implicit Gauss-Seidel fractional iteration. Such a method is of first-order accuracy in time and...
Changjian Xie
VirPro: Visual-referred Probabilistic Prompt Learning for Weakly-Supervised Monocular 3D Detection
Monocular 3D object detection typically relies on pseudo-labeling techniques to reduce dependency on real-world annotations. Recent advances demonstrate that deterministic linguistic cues can serve...
Chupeng Liu, Jiyong Rao, Shangquan Sun, Runkai Zhao, Weidong Cai
Efficient Soft Actor-Critic with LLM-Based Action-Level Guidance for Continuous Control
We present GuidedSAC, a novel reinforcement learning (RL) algorithm that facilitates efficient exploration in vast state-action spaces. GuidedSAC leverages large language models (LLMs) as intellige...
Hao Ma, Zhiqiang Pu, Xiaolin Ai, Huimu Wang
Entrainment of magnetic fluid by a moving boundary of a plane gap
A fluid mechanics problem is solved which technological prototype is a fluid acoustic contact that is an inherent element of ultrasonic non-destructive testing procedures. It is well known that the...
Denis S. Goldobin, Yuriy L. Raikher
Multi-stage Flow Scheduling for LLM Serving
Meeting stringent Time-To-First-Token (TTFT) requirements is crucial for LLM applications. To improve efficiency, modern LLM serving systems adopt disaggregated architectures with diverse paralleli...
Yijun Sun, Xudong Liao, Songrun Xie, Hao Chen, Han Tian, Wenxue Li, Yiming Zhang, Kai Chen
VLM2Rec: Resolving Modality Collapse in Vision-Language Model Embedders for Multimodal Sequential Recommendation
Sequential Recommendation (SR) in multimodal settings typically relies on small frozen pretrained encoders, which limits semantic capacity and prevents Collaborative Filtering (CF) signals from bei...
Junyoung Kim, Woojoo Kim, Jaehyung Lim, Dongha Kim, Hwanjo Yu
Why the Future Is Not Trading: Causally Inert Events as a Test for Time Travelers
We present an empirical argument against the existence of single timeline backward time travel using the price behavior of prediction markets. If rational agents could travel backward in time, bina...
David Awad
Large Language Models as a Semantic Interface and Ethical Mediator in Neuro-Digital Ecosystems: Conceptual Foundations and a Regulatory Imperative
This article introduces and substantiates the concept of Neuro-Linguistic Integration (NLI), a novel paradigm for human-technology interaction where Large Language Models (LLMs) act as a key semant...
Alexander V. Shenderuk-Zhidkov, Alexander E. Hramov
ZipServ: Fast and Memory-Efficient LLM Inference with Hardware-Aware Lossless Compression
Lossless model compression holds tremendous promise for alleviating the memory and bandwidth bottlenecks in bit-exact Large Language Model (LLM) serving. However, existing approaches often result i...
Ruibo Fan, Xiangrui Yu, Xinglin Pan, Zeyu Li, Weile Luo, Qiang Wang, Wei Wang, Xiaowen Chu
Argument Reconstruction as Supervision for Critical Thinking in LLMs
To think critically about arguments, human learners are trained to identify, reconstruct, and evaluate arguments. Argument reconstruction is especially important because it makes an argument's unde...
Hyun Ryu, Gyouk Chu, Gregor Betz, Eunho Yang, Carolyn Rose, Sean Welleck
SafeLand: Safe Autonomous Landing in Unknown Environments with Bayesian Semantic Mapping
Autonomous landing of uncrewed aerial vehicles (UAVs) in unknown, dynamic environments poses significant safety challenges, particularly near people and infrastructure, as UAVs transition to routin...
Markus Gross, Andreas Greiner, Sai Bharadhwaj Matha, Felix Soest, Daniel Cremers, Henri Meeß