Papers
Research papers from arXiv and related sources
A variational multi-phase model for elastoplastic materials with microstructure evolution
A general model is formulated for elasto-plastic materials undergoing linear kinematic hardening to describe microstructure evolution associated with phase transformations. Using infinitesimal stra...
Sarah Dinkelacker-Steinhoff, Klaus Hackl
What Do LLMs Associate with Your Name? A Human-Centered Black-Box Audit of Personal Data
Large language models (LLMs), and conversational agents based on them, are exposed to personal data (PD) during pre-training and during user interactions. Prior work shows that PD can resurface, ye...
Dimitri Staufer, Kirsten Morehouse
ShadAR: LLM-driven shader generation to transform visual perception in Augmented Reality
Augmented Reality (AR) can simulate various visual perceptions, such as how individuals with colorblindness see the world. However, these simulations require developers to predefine each visual eff...
Yanni Mei, Samuel Wendt, Florian Mueller, Jan Gugenheimer
Small LLMs for Medical NLP: a Systematic Analysis of Few-Shot, Constraint Decoding, Fine-Tuning and Continual Pre-Training in Italian
Large Language Models (LLMs) consistently excel in diverse medical Natural Language Processing (NLP) tasks, yet their substantial computational requirements often limit deployment in real-world hea...
Pietro Ferrazzi, Mattia Franzin, Alberto Lavelli, Bernardo Magnini
Optically Sensorized Electro-Ribbon Actuator (OS-ERA)
Electro-Ribbon Actuators (ERAs) are lightweight flexural actuators that exhibit ultrahigh displacement and fast movement. However, their embedded sensing relies on capacitive sensors with limited p...
Carolina Gay, Petr Trunin, Diana Cafiso, Yuejun Xu, Majid Taghavi, Lucia Beccai
Auditing Reciprocal Sentiment Alignment: Inversion Risk, Dialect Representation and Intent Misalignment in Transformers
The core theme of bidirectional alignment is ensuring that AI systems accurately understand human intent and that humans can trust AI behavior. However, this loop fractures significantly across lan...
Nusrat Jahan Lia, Shubhashis Roy Dipta
Entropy-Based Data Selection for Language Models
Modern language models (LMs) increasingly require two critical resources: computational resources and data resources. Data selection techniques can effectively reduce the amount of training data re...
Hongming Li, Yang Liu, Chao Huang
The CTI Echo Chamber: Fragmentation, Overlap, and Vendor Specificity in Twenty Years of Cyber Threat Reporting
Despite the high volume of open-source Cyber Threat Intelligence (CTI), our understanding of long-term threat actor-victim dynamics remains fragmented due to the lack of structured datasets and inc...
Manuel Suarez-Roman, Francesco Marciori, Mauro Conti, Juan Tapiador
Privacy in Theory, Bugs in Practice: Grey-Box Auditing of Differential Privacy Libraries
Differential privacy (DP) implementations are notoriously prone to errors, with subtle bugs frequently invalidating theoretical guarantees. Existing verification methods are often impractical: form...
Tudor Cebere, David Erb, Damien Desfontaines, Aurélien Bellet, Jack Fitzsimons
Jolt Atlas: Verifiable Inference via Lookup Arguments in Zero Knowledge
We present Jolt Atlas, a zero-knowledge machine learning (zkML) framework that extends the Jolt proving system to model inference. Unlike zkVMs (zero-knowledge virtual machines), which emulate CPU ...
Wyatt Benno, Alberto Centelles, Antoine Douchet, Khalil Gibran
Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research
Web research and practices have evolved significantly over time, offering users diverse and accessible solutions across a wide range of tasks. While advanced concepts such as Web 4.0 have emerged f...
Amirereza Abbasi, Mohsen Hooshmand
ACOS: Arrays of Cheap Optical Switches
Machine learning training places immense demands on cluster networks, motivating specialized architectures and co-design with parallelization strategies. Recent designs incorporating optical circui...
Daniel Amir, Ori Cohen, Jakob Krebs, Mark Silberstein
Do Hackers Dream of Electric Teachers?: A Large-Scale, In-Situ Evaluation of Cybersecurity Student Behaviors and Performance with AI Tutors
To meet the ever-increasing demands of the cybersecurity workforce, AI tutors have been proposed for personalized, scalable education. But, while AI tutors have shown promise in introductory progra...
Michael Tompkins, Nihaarika Agarwal, Ananta Soneji, Robert Wasinger, Connor Nelson, Kevin Leach, ...
ABCD: All Biases Come Disguised
Multiple-choice question (MCQ) benchmarks have been a standard evaluation practice for measuring LLMs' ability to reason and answer knowledge-based questions. Through a synthetic NonsenseQA benchma...
Mateusz Nowak, Xavier Cadet, Peter Chin
AIDG: Evaluating Asymmetry Between Information Extraction and Containment in Multi-Turn Dialogue
Evaluating the strategic reasoning capabilities of Large Language Models (LLMs) requires moving beyond static benchmarks to dynamic, multi-turn interactions. We introduce AIDG (Adversarial Informat...
Adib Sakhawat, Fardeen Sadab, Rakin Shahriar
WarpRec: Unifying Academic Rigor and Industrial Scale for Responsible, Reproducible, and Efficient Recommendation
Innovation in Recommender Systems is currently impeded by a fractured ecosystem, where researchers must choose between the ease of in-memory experimentation and the costly, complex rewriting requir...
Marco Avolio, Potito Aghilar, Sabino Roccotelli, Vito Walter Anelli, Chiara Mallamaci, Vincenzo P...
Preserving Historical Truth: Detecting Historical Revisionism in Large Language Models
Large language models (LLMs) are increasingly used as sources of historical information, motivating the need for scalable audits on contested events and politically charged narratives in settings t...
Francesco Ortu, Joeun Yook, Punya Syon Pandey, Keenan Samway, Bernhard Schölkopf, Alberto Cazzani...
Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study
Uncertainty quantification has emerged as an effective approach to closed-book hallucination detection for LLMs, but existing methods are largely designed for short-form outputs and do not generali...
Dylan Bouchard, Mohit Singh Chauhan, Viren Bajaj, David Skarbrevik
Evaluating Extremely Low-Resource Machine Translation: A Comparative Study of ChrF++ and BLEU Metrics
Evaluating machine translation (MT) quality in extremely low-resource language (ELRL) scenarios poses unique challenges, as widely used metrics such as BLEU, effective in high-resource settings, of...
Sanjeev Kumar, Preethi Jyothi, Pushpak Bhattacharyya
Convergence Analysis of Two-Layer Neural Networks under Gaussian Input Masking
We investigate the convergence guarantee of two-layer neural network training with Gaussian randomly masked inputs. This scenario corresponds to Gaussian dropout at the input level, or noisy input ...
Afroditi Kolomvaki, Fangshuo Liao, Evan Dramko, Ziyun Guang, Anastasios Kyrillidis