Papers
Research papers from arXiv and related sources
A Contextual Help Browser Extension to Assist Digital Illiterate Internet Users
This paper describes the design, implementation, and evaluation of a browser extension that provides contextual help to users who hover over technological acronyms and abbreviations on web pages. T...
Christos Koutsiaris
From Isolated Scoring to Collaborative Ranking: A Comparison-Native Framework for LLM-Based Paper Evaluation
Large language models (LLMs) are currently applied to scientific paper evaluation by assigning an absolute score to each paper independently. However, since score scales vary across conferences, ti...
Pujun Zheng, Jiacheng Yao, Jinquan Zheng, Chenyang Gu, Guoxiu He, Jiawei Liu, Yong Huang, Tianrui...
Negation is Not Semantic: Diagnosing Dense Retrieval Failure Modes for Trade-offs in Contradiction-Aware Biomedical QA
Large Language Models (LLMs) have demonstrated strong capabilities in biomedical question answering, yet their tendency to generate plausible but unverified claims poses serious risks in clinical s...
Soumya Ranjan Sahoo, Gagan N., Sanand Sasidharan, Divya Bharti
One-Step Sampler for Boltzmann Distributions via Drifting
We present a drifting-based framework for amortized sampling of Boltzmann distributions defined by energy functions. The method trains a one-step neural generator by projecting samples along a Gaus...
Wenhan Cao, Keyu Yan, Lin Zhao
LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentation
Precise localization and delineation of brain tumors using Magnetic Resonance Imaging (MRI) are essential for planning therapy and guiding surgical decisions. However, most existing approaches rely...
Mohammad Robaitul Islam Bhuiyan, Sheethal Bhat, Melika Qahqaie, Tri-Thien Nguyen, Paula Andrea Pé...
KA2L: A Knowledge-Aware Active Learning Framework for LLMs
Fine-tuning large language models (LLMs) with high-quality knowledge has been shown to enhance their performance effectively. However, there is a paucity of research on the depth of domain-specific...
Haoxuan Yin, Bojian Liu, Chen Tang, Yangfan Wang, Lian Yan, Jingchi Jiang
In Trust We Survive: Emergent Trust Learning
We introduce Emergent Trust Learning (ETL), a lightweight, trust-based control algorithm that can be plugged into existing AI agents. It enables these to reach cooperation in competitive game envir...
Qianpu Chen, Giulio Barbero, Mike Preuss, Derya Soydaner
Forecasting Sensitivity to Modified Dispersion Effects in Pulsar Timing Arrays
The pulsar timing array systems have reported a detection of a nanohertz-band stochastic gravitational wave background in our galaxy. It is of interest to use this observation to probe modified gra...
Jonathan Grée, Qiuyue Liang, Elisa G. M. Ferreira
Zipper-LoRA: Dynamic Parameter Decoupling for Speech-LLM based Multilingual Speech Recognition
Speech Large Language Models (Speech-LLMs) have emerged as a powerful approach for automatic speech recognition (ASR) by aligning speech encoders with large language models. However, adapting these...
Yuxiang Mei, Delai Qiu, Shengping Liu, Jiaen Liang, Yanhua Long
Prompt-Free Universal Region Proposal Network
Identifying potential objects is critical for object recognition and analysis across various computer vision applications. Existing methods typically localize potential objects by relying on exempl...
Qihong Tang, Changhan Liu, Shaofeng Zhang, Wenbin Li, Qi Fan, Yang Gao
Deep Learning-Based Airway Segmentation in Systemic Lupus Erythematosus Patients with Interstitial Lung Disease (SLE-ILD): A Comparative High-Resolution CT Analysis
To characterize lobar and segmental airway volume differences between systemic lupus erythematosus (SLE) patients with interstitial lung disease (ILD) and those without ILD (non-ILD) using a deep l...
Sirong Piao, Ying Ming, Ruijie Zhao, Jiaru Wang, Ran Xiao, Rui Zhao, Zicheng Liao, Qiqi Xu, Shaoz...
Data-Driven Estimation of Vinnicombe metric
Quantifying model mismatch in a control-relevant manner is fundamental in robust control. A well-known metric for this purpose is the $ν$-gap, or Vinnicombe metric, which measures the discrepancy b...
Margarita A. Guerrero, Henrik Sandberg, Cristian R. Rojas
Deploying Semantic ID-based Generative Retrieval for Large-Scale Podcast Discovery at Spotify
Podcast listening is often grounded in a set of favorite shows, while listener intent can evolve over time. This combination of stable preferences and changing intent motivates recommendation appro...
Edoardo D'Amico, Marco De Nadai, Praveen Chandar, Divita Vohra, Shawn Lin, Max Lefarov, Paul Gigi...
Informative Semi-Factuals for XAI: The Elaborated Explanations that People Prefer
Recently, in eXplainable AI (XAI), $\textit{even if}$ explanations -- so-called semi-factuals -- have emerged as a popular strategy that explains how a predicted outcome $\textit{can remain the sam...
Saugat Aryal, Mark T. Keane
A Unified Language Model for Large Scale Search, Recommendation, and Reasoning
LLMs are increasingly applied to recommendation, retrieval, and reasoning, yet deploying a single end-to-end model that can jointly support these behaviors over large, heterogeneous catalogs remain...
Marco De Nadai, Edoardo D'Amico, Max Lefarov, Alexandre Tamborrino, Divita Vohra, Mark VanMiddles...
Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer
Accurate prediction of permeability tensors from pore-scale microstructure images is essential for subsurface flow modeling, yet direct numerical simulation requires hours per sample, fundamentally...
Mohammad Nooraiepour
Rel-Zero: Harnessing Patch-Pair Invariance for Robust Zero-Watermarking Against AI Editing
Recent advancements in diffusion-based image editing pose a significant threat to the authenticity of digital visual content. Traditional embedding-based watermarking methods often introduce percep...
Pengzhen Chen, Yanwei Liu, Xiaoyan Gu, Xiaojun Chen, Wu Liu, Weiping Wang
Photonuclear reactions on stable isotopes of cadmium and tellurium at bremsstrahlung end-point energies of 10-23 MeV
This work used the γ-activation approach to conduct tests at bremsstrahlung end-point energies of 10-23 MeV utilising the MT-25 microtron beam. The experimental values of relative yields and cross ...
F. A. Rasulova, A. A. Kuznetsov, V. O. Nesterenko, J. H. Khushvaktov, S. I. Alekseev, N. Yu. Furs...
Translation Invariance of Neural Operators for the FitzHugh-Nagumo Model
Neural Operators (NOs) are a powerful deep learning framework designed to learn the solution operator that arise from partial differential equations. This study investigates NOs ability to capture ...
Luca Pellegrini
Detecting the Machine: A Comprehensive Benchmark of AI-Generated Text Detectors Across Architectures, Domains, and Adversarial Conditions
The rapid proliferation of large language models (LLMs) has created an urgent need for robust and generalizable detectors of machine-generated text. Existing benchmarks typically evaluate a single ...
Madhav S. Baidya, S. S. Baidya, Chirag Chawla