Papers
Research papers from arXiv and related sources
SEALing the Gap: A Reference Framework for LLM Inference Carbon Estimation via Multi-Benchmark Driven Embodiment
Large Language Models are rapidly gaining traction in software engineering, yet their growing carbon footprint raises pressing sustainability concerns. While training emissions are substantial, inf...
Priyavanshi Pathania, Rohit Mehra, Vibhu Saujanya Sharma, Vikrant Kaulgud, Tiffani Nevels, Sanjay...
Reducing Labeling Effort in Architecture Technical Debt Detection through Active Learning and Explainable AI
Self-Admitted Technical Debt (SATD) refers to technical compromises explicitly admitted by developers in natural language artifacts such as code comments, commit messages, and issue trackers. Among...
Edi Sutoyo, Paris Avgeriou, Andrea Capiluppi
ShipTraj-R1: Reinforcing Ship Trajectory Prediction in Large Language Models via Group Relative Policy Optimization
Recent advancements in reinforcement fine-tuning have significantly improved the reasoning ability of large language models (LLMs). In particular, methods such as group relative policy optimization...
Yang Zhan, Yunhao Li, Zhang Chao, Yuxu Lu, Yan Li
Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models
Graph-based tasks in the zero-shot setting remain a significant challenge due to data scarcity and the inability of traditional Graph Neural Networks (GNNs) to generalize to unseen domains or label...
Fengzhi Li, Liang Zhang, Yuan Zuo, Ruiqing Zhao, YanSong Liu, Yunfei Ma, Fanyu Meng, Junlan Feng
A signal dedispersion algorithm for imaging-based transient searches
Dedispersion is the computational process of correcting for the frequency-dependent time delay affecting a radio signal that propagates through the interstellar and intergalactic media. It is a cru...
Cristian Di Pietrantonio, Marcin Sokolowski, Christopher Harris, Danny C. Price, Randall Wayth
LOO-PIT predictive model checking
We consider predictive checking for Bayesian model assessment using leave-one-out probability integral transform (LOO-PIT). LOO-PIT values are conditional cumulative predictive probabilities given ...
Herman Tesso, Aki Vehtari
GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights
Glomerular pathology is central to the diagnosis and prognosis of renal diseases, yet the heterogeneity of glomerular morphology and fine-grained lesion patterns remain challenging for current AI a...
Qiming He, Jing Li, Tian Guan, Yifei Ma, Zimo Zhao, Yanxia Wang, Hongjing Chen, Yingming Xu, Shua...
Does Fine-tuning by Reinforcement Learning Improve Generalization in Binary Speech Deepfake Detection?
Building speech deepfake detection models that are generalizable to unseen attacks remains a challenging problem. Although the field has shifted toward a pre-training and fine-tuning paradigm using...
Xin Wang, Ge Wanying, Junichi Yamagishi
Eliciting Numerical Predictive Distributions of LLMs Without Autoregression
Large Language Models (LLMs) have recently been successfully applied to regression tasks -- such as time series forecasting and tabular prediction -- by leveraging their in-context learning abiliti...
Julianna Piskorz, Katarzyna Kobalczyk, Mihaela van der Schaar
Articulation in Motion: Prior-free Part Mobility Analysis for Articulated Objects By Dynamic-Static Disentanglement
Articulated objects are ubiquitous in daily life. Our goal is to achieve a high-quality reconstruction, segmentation of independent moving parts, and analysis of articulation. Recent methods analys...
Hao Ai, Wenjie Chang, Jianbo Jiao, Ales Leonardis, Ofek Eyal
Learning to Generate and Extract: A Multi-Agent Collaboration Framework For Zero-shot Document-level Event Arguments Extraction
Document-level event argument extraction (DEAE) is essential for knowledge acquisition, aiming to extract participants of events from documents.In the zero-shot setting, existing methods employ LLM...
Guangjun Zhang, Hu Zhang, Yazhou Han, Yue Fan, Yuhang Shao, Ru Li, Hongye Tan
SAE as a Crystal Ball: Interpretable Features Predict Cross-domain Transferability of LLMs without Training
In recent years, pre-trained large language models have achieved remarkable success across diverse tasks. Besides the pivotal role of self-supervised pre-training, their effectiveness in downstream...
Qi Zhang, Yifei Wang, Xiaohan Wang, Jiajun Chai, Guojun Yin, Wei Lin, Yisen Wang
Speech recognition assisted by large language models to command software orally -- Application to an augmented and virtual reality web app for immersive molecular graphics
This project successfully developed, evaluated and integrated a Voice User Interface (VUI) into a web application that we are developing for immersive molecular graphics. Said app provides augmente...
Fabio Cortes Rodriguez, Luciano Abriata
Embedding interpretable $\ell_1$-regression into neural networks for uncovering temporal structure in cell imaging
While artificial neural networks excel in unsupervised learning of non-sparse structure, classical statistical regression techniques offer better interpretability, in particular when sparseness is ...
Fabian Kabus, Maren Hackenberg, Julia Hindel, Thibault Cholvin, Antje Kilias, Thomas Brox, Abhina...
SpecLoop: An Agentic RTL-to-Specification Framework with Formal Verification Feedback Loop
RTL implementations frequently lack up-to-date or consistent specifications, making comprehension, maintenance, and verification costly and error-prone. While prior work has explored generating spe...
Fu-Chieh Chang, Yu-Hsin Yang, Hung-Ming Huang, Yun-Chia Hsu, Yin-Yu Lin, Ming-Fang Tsai, Chun-Chi...
Kraken: Higher-order EM Side-Channel Attacks on DNNs in Near and Far Field
The multi-million dollar investment required for modern machine learning (ML) has made large ML models a prime target for theft. In response, the field of model stealing has emerged. Attacks based ...
Peter Horvath, Ilia Shumailov, Lukasz Chmielewski, Lejla Batina, Yuval Yarom
LLandMark: A Multi-Agent Framework for Landmark-Aware Multimodal Interactive Video Retrieval
The increasing diversity and scale of video data demand retrieval systems capable of multimodal understanding, adaptive reasoning, and domain-specific knowledge integration. This paper presents LLa...
Minh-Chi Phung, Thien-Bao Le, Cam-Tu Tran-Thi, Thu-Dieu Nguyen-Thi, Vu-Hung Dao
MuxTune: Efficient Multi-Task LLM Fine-Tuning in Multi-Tenant Datacenters via Spatial-Temporal Backbone Multiplexing
Parameter-Efficient Fine-Tuning (PEFT) is widely applied as the backend of fine-tuning APIs for large language model (LLM) customization in datacenters. Service providers deploy separate instances ...
Chunyu Xue, Yi Pan, Weihao Cui, Quan Chen, Shulai Zhang, Bingsheng He, Minyi Guo
SIGMark: Scalable In-Generation Watermark with Blind Extraction for Video Diffusion
Artificial Intelligence Generated Content (AIGC), particularly video generation with diffusion models, has been advanced rapidly. Invisible watermarking is a key technology for protecting AI-genera...
Xinjie Zhu, Zijing Zhao, Hui Jin, Qingxiao Guo, Yilong Ma, Yunhao Wang, Xiaobing Guo, Weifeng Zhang
Emerging trends in Cislunar Space for Lunar Science Exploration and Space Robotics aiding Human Spaceflight Safety
In recent years, the Moon has emerged as an unparalleled extraterrestrial testbed for advancing cuttingedge technological and scientific research critical to enabling sustained human presence on it...
Arsalan Muhammad, Yue Wang, Hai Huang, Hao Wang