Tootfinder

ReTraceQA: Evaluating Reasoning Traces of Small Language Models in Commonsense Question Answering
Francesco Maria Molfese, Luca Moroni, Ciro Porcaro, Simone Conia, Roberto Navigli
https://arxiv.org/abs/2510.09351

ReTraceQA: Evaluating Reasoning Traces of Small Language Models in Commonsense Question Answering
While Small Language Models (SLMs) have demonstrated promising performance on an increasingly wide array of commonsense reasoning benchmarks, current evaluation practices rely almost exclusively on the accuracy of their final answers, neglecting the validity of the reasoning processes that lead to those answers. To address this issue, we introduce ReTraceQA, a novel benchmark that introduces process-level evaluation for commonsense reasoning tasks. Our expert-annotated dataset reveals that in a…

A Fast-Converging Decentralized Approach to the Weighted Minimum Vertex Cover Problem
Matteo Mordacchini, Emanuele Carlini, Patrizio Dazzi
https://arxiv.org/abs/2510.11697 https…

A Fast-Converging Decentralized Approach to the Weighted Minimum Vertex Cover Problem
We address the problem of computing a Minimum Weighted Vertex Cover (MWVC) in a decentralized network. MWVC, a classical NP-hard problem, is foundational in applications such as network monitoring and resource placement. We propose a fully decentralized protocol where each node makes decisions using only local knowledge and communicates with its neighbors. The method is adaptive, communication-efficient, and avoids centralized coordination. We evaluate the protocol on real-world and synthetic g…

LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering
Jielin Qiu, Zuxin Liu, Zhiwei Liu, Rithesh Murthy, Jianguo Zhang, Haolin Chen, Shiyu Wang, Ming Zhu, Liangwei Yang, Juntao Tan, Zhepeng Cen, Cheng Qian, Shelby Heinecke, Weiran Yao, Silvio Savarese, Caiming Xiong, Huan Wang
https://arxiv.org/abs/2…

LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering
The emergence of long-context language models with context windows extending to millions of tokens has created new opportunities for sophisticated code understanding and software development evaluation. We propose LoCoBench, a comprehensive benchmark specifically designed to evaluate long-context LLMs in realistic, complex software development scenarios. Unlike existing code evaluation benchmarks that focus on single-function completion or short-context tasks, LoCoBench addresses the critical e…

Post-Quantum Cryptography and Quantum-Safe Security: A Comprehensive Survey
Gaurab Chhetri, Shriyank Somvanshi, Pavan Hebli, Shamyo Brotee, Subasish Das
https://arxiv.org/abs/2510.10436

Post-Quantum Cryptography and Quantum-Safe Security: A Comprehensive Survey
Post-quantum cryptography (PQC) is moving from evaluation to deployment as NIST finalizes standards for ML-KEM, ML-DSA, and SLH-DSA. This survey maps the space from foundations to practice. We first develop a taxonomy across lattice-, code-, hash-, multivariate-, isogeny-, and MPC-in-the-Head families, summarizing security assumptions, cryptanalysis, and standardization status. We then compare performance and communication costs using representative, implementation-grounded measurements, and re…

Designing and Evaluating an AI-driven Immersive Multidisciplinary Simulation (AIMS) for Interprofessional Education
Ruijie Wang, Jie Lu, Bo Pei, Evonne Jones, Jamey Brinson, Timothy Brown
https://arxiv.org/abs/2510.08891

Designing and Evaluating an AI-driven Immersive Multidisciplinary Simulation (AIMS) for Interprofessional Education
Interprofessional education has long relied on case studies and the use of standardized patients to support teamwork, communication, and related collaborative competencies among healthcare professionals. However, traditional approaches are often limited by cost, scalability, and inability to mimic the dynamic complexity of real-world clinical scenarios. To address these challenges, we designed and developed AIMS (AI-Enhanced Immersive Multidisciplinary Simulations), a virtual simulation that in…

Effects of automotive microphone frequency response characteristics and noise conditions on speech and ASR quality -- an experimental evaluation
Michele Buccoli, Yu Du, Jacob Soendergaard, Simone Shawn Cazzaniga
https://arxiv.org/abs/2510.09236

Effects of automotive microphone frequency response characteristics and noise conditions on speech and ASR quality -- an experimental evaluation
Upon choosing microphones for automotive hands-free communication or Automatic Speech Recognition (ASR) applications, OEMs typically specify wideband, super wideband or even fullband requirements following established standard recommendations (e.g., ITU-P.1110, ITU-P.1120). In practice, it is often challenging to achieve the preferred bandwidth for an automotive microphone when considering limitations and constraints on microphone placement inside the cabin, and the automotive grade environment…

Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems
Mohammud J. Bocus, Senhui Qiu, Robert J. Piechocki, Kerstin Eder
https://arxiv.org/abs/2510.09842

Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems
Energy efficiency has emerged as a defining constraint in the evolution of sustainable Internet of Things (IoT) networks. This work moves beyond simulation-based or device-centric studies to deliver measurement-driven, network-level smart energy analysis. The proposed system enables end-to-end visibility of energy flows across distributed IoT infrastructures, uniting Bluetooth Low Energy (BLE) and Visible Light Communication (VLC) modes with environmental sensing and E-ink display subsystems un…

Culturally-Aware Conversations: A Framework & Benchmark for LLMs
Shreya Havaldar, Sunny Rai, Young-Min Cho, Lyle Ungar
https://arxiv.org/abs/2510.11563 https://

Culturally-Aware Conversations: A Framework & Benchmark for LLMs
Existing benchmarks that measure cultural adaptation in LLMs are misaligned with the actual challenges these models face when interacting with users from diverse cultural backgrounds. In this work, we introduce the first framework and benchmark designed to evaluate LLMs in realistic, multicultural conversational settings. Grounded in sociocultural theory, our framework formalizes how linguistic style - a key element of cultural communication - is shaped by situational, relational, and cultural …

DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge
Zonghai Yao, Michael Sun, Won Seok Jang, Sunjae Kwon, Soie Kwon, Hong Yu
https://arxiv.org/abs/2509.07188 …

DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge
Discharge communication is a critical yet underexplored component of patient care, where the goal shifts from diagnosis to education. While recent large language model (LLM) benchmarks emphasize in-visit diagnostic reasoning, they fail to evaluate models' ability to support patients after the visit. We introduce DischargeSim, a novel benchmark that evaluates LLMs on their ability to act as personalized discharge educators. DischargeSim simulates post-visit, multi-turn conversations between LLM-…