Large Language Models (LLMs) are increasingly applied in various science domains, yet their broader adoption remains constrained by a critical challenge: the lack of trustworthy, verifiable outputs. Current LLMs often generate answers without reliable source attribution, or worse, with incorrect attributions, posing a barrier to their use in scientific and high-stakes settings, where traceability and accountability are paramount. To be reliable, attribution systems require high accuracy for short-length attribution on retrieved data, i.e., attribution to a sentence within a document rather than the entire document. We propose SAFE, a Sentence-level A ttribution FramEwork for Retrieve-Augmented Generation (RAG) systems that attributes generated sentences during generation. This allows users to verify sentences as they read them and correct the model when the attribution indicates the generated text is not grounded in the documents, increasing the safety of LLM systems. This framework consists of two steps: predicting the required number of references for a sentence, and attributing the sentence. Our approach achieved 95% accuracy in the first step, which translated to 2.1\~6.0% improvements in the accuracy (normalized for maximum possible accuracy) of all attribution algorithms in our clean dataset, when compared to their top-1 accuracy. We also applied SAFE in real-world scenarios with documents containing hundreds to thousands of sentences. In these settings, SAFE reliably attributed sentences to their source documents, demonstrating that the method generalizes beyond controlled benchmarks. The SAFE framework and the training dataset are publicly available on GitHub.

This research presents a novel method for non-invasive arterial blood pressure (ABP) prediction using speech signals, employing a BERT -based regression model. Arterial blood pressure is a vital indicator of cardiovascular health, and accurate monitoring is essential in preventing hypertension-related complications. Traditional cuff-based methods often yield inconsistent results due to factors like white-coat and masked hypertension. Our approach leverages the acoustic characteristics of speech, capturing voice features to establish correlations with blood pressure levels. Utilizing advanced deep learning techniques, we analyze speech signals to extract relevant patterns, enabling real-time monitoring without the discomfort of conventional methods.

Given the inherent limitations of traditional Variational Quantum Eigensolver(VQE) algorithms, the integration of deep generative models into hybrid quantum-classical frameworks, specifically the Generative Quantum Eigensolver(GQE), represents a promising innovative approach. However, taking the Unitary Coupled Cluster with Singles and Doubles(UCCSD) ansatz which is widely used in quantum chemistry as an example, different molecular systems require constructions of distinct quantum operators. Considering the similarity of different molecules, the construction of quantum operators utilizing the similarity can reduce the computational cost significantly. Inspired by the SMILES representation method in computational chemistry, we developed a text-based representation approach for UCCSD quantum operators by leveraging the inherent representational similarities between different molecular systems. This framework explores text pattern similarities in quantum operators and employs text similarity metrics to establish a transfer learning framework. Our approach with a naive baseline setting demonstrates knowledge transfer between different molecular systems for ground-state energy calculations within the GQE paradigm. This discovery offers significant benefits for hybrid quantum-classical computation of molecular ground-state energies, substantially reducing computational resource requirements.

Pedestrian safety is a critical component of urban mobility and is strongly influenced by the interactions between pedestrian decision-making and driver yielding behavior at crosswalks. Modeling driver--pedestrian interactions at intersections requires accurately capturing the complexity of these behaviors. Traditional machine learning models often struggle to capture the nuanced and context-dependent reasoning required for these multifactorial interactions, due to their reliance on fixed feature representations and limited interpretability. In contrast, large language models (LLMs) are suited for extracting patterns from heterogeneous traffic data, enabling accurate modeling of driver-pedestrian interactions. Therefore, this paper leverages multimodal LLMs through a novel prompt design that incorporates domain-specific knowledge, structured reasoning, and few-shot prompting, enabling interpretable and context-aware inference of driver yielding behavior, as an example application of modeling pedestrian--driver interaction. We benchmarked state-of-the-art LLMs against traditional classifiers, finding that GPT-4o consistently achieves the highest accuracy and recall, while Deepseek-V3 excels in precision. These findings highlight the critical trade-offs between model performance and computational efficiency, offering practical guidance for deploying LLMs in real-world pedestrian safety systems.

Abstract--As the fifth-generation (5G) mobile communication system continues its global deployment, both industry and academia have started conceptualizing the 6th generation (6G) to address the growing need for a progressively advanced and digital society. Even while 5G offers considerable advancements over L TE, it could struggle to be sufficient to meet all of the requirements, including ultra-high reliability, seamless automation, and ubiquitous coverage. In response, 6G is supposed to bring out a highly intelligent, automated, and ultra-reliable communication system that can handle a vast number of connected devices. This paper offers a comprehensive overview of 6G, beginning with its main stringent requirements while focusing on key enabling technologies such as terahertz (THz) communications, intelligent reflecting surfaces, massive MIMO and AI-driven networking that will shape the 6G networks. Furthermore, the paper lists various 6G applications and usage scenarios that will benefit from these advancements. At the end, we outline the potential challenges that must be addressed to achieve the 6G promises. Keywords-- 6 G, Usage Scenarios, Capabilities, Enabling technologies, Challenges. I. INTRODUCTION The wireless industry has continuously evolved and it is is one of the few industry sectors that have kept a fast-growing trend, with each generation introducing higher frequencies, larger bandwidths, and faster data rates [1]. Since Marconi's wireless telegraphy in the 19th century, mobile networks have advanced from 1G's basic voice services to 5G's ultra-high-definition 3D data transmission. Researchers are currently focusing on 6G as 5G deployment expands throughout the world and is anticipated to be realized by 2030.

As academic paper publication numbers grow exponentially, conducting in-depth surveys with LLMs automatically has become an inevitable trend. Outline writing, which aims to systematically organize related works, is critical for automated survey generation. Yet existing automatic survey methods treat outline writing as mere workflow steps in the overall pipeline. Such template-based workflows produce outlines that lack in-depth understanding of the survey topic and fine-grained styles. To address these limitations, we propose Meow, the first metadata-driven outline writing framework that produces organized and faithful outlines efficiently. Specifically, we first formulate outline writing as an end-to-end task that generates hierarchical structured outlines from paper metadata. We then curate a high-quality dataset of surveys from arXiv, bioRxiv, and medRxiv, and establish systematic evaluation metrics for outline quality assessment. Finally, we employ a two-stage training approach combining supervised fine-tuning and reinforcement learning. Our 8B reasoning model demonstrates strong performance with high structural fidelity and stylistic coherence.

Over the past decade, Computational Linguistics (CL) and Natural Language Processing (NLP) have evolved rapidly, especially with the advent of Transformer-based Large Language Models (LLMs). This shift has transformed research goals and priorities, from Lexical and Semantic Resources to Language Modelling and Multimodality. In this study, we track the research trends of the Italian CL and NLP community through an analysis of the contributions to CLiC-it, arguably the leading Italian conference in the field. We compile the proceedings from the first 10 editions of the CLiC-it conference (from 2014 to 2024) into the CLiC-it Corpus, providing a comprehensive analysis of both its metadata, including author provenance, gender, affiliations, and more, as well as the content of the papers themselves, which address various topics. Our goal is to provide the Italian and international research communities with valuable insights into emerging trends and key developments over time, supporting informed decisions and future directions in the field.

The prevailing ``trivia-centered paradigm'' for evaluating the cultural alignment of large language models (LLMs) is increasingly inadequate as these models become more advanced and widely deployed. Existing approaches typically reduce culture to static facts or values, testing models via multiple-choice or short-answer questions that treat culture as isolated trivia. Such methods neglect the pluralistic and interactive realities of culture, and overlook how cultural assumptions permeate even ostensibly ``neutral'' evaluation settings. In this position paper, we argue for \textbf{intentionally cultural evaluation}: an approach that systematically examines the cultural assumptions embedded in all aspects of evaluation, not just in explicitly cultural tasks. We systematically characterize the what, how, and circumstances by which culturally contingent considerations arise in evaluation, and emphasize the importance of researcher positionality for fostering inclusive, culturally aligned NLP research. Finally, we discuss implications and future directions for moving beyond current benchmarking practices, discovering important applications that we don't know exist, and involving communities in evaluation design through HCI-inspired participatory methodologies.

Voluntary commitments are central to international AI governance, as demonstrated by recent voluntary guidelines from the White House to the G7, from Bletchley Park to Seoul. How do major AI companies make good on their commitments? We score companies based on their publicly disclosed behavior by developing a detailed rubric based on their eight voluntary commitments to the White House in 2023. We find significant heterogeneity: while the highest-scoring company (OpenAI) scores a 83% overall on our rubric, the average score across all companies is just 53%. The companies demonstrate systemically poor performance for their commitment to model weight security with an average score of 17%: 11 of the 16 companies receive 0% for this commitment. Our analysis highlights a clear structural shortcoming that future AI governance initiatives should correct: when companies make public commitments, they should proactively disclose how they meet their commitments to provide accountability, and these disclosures should be verifiable. To advance policymaking on corporate AI governance, we provide three directed recommendations that address underspecified commitments, the role of complex AI supply chains, and public transparency that could be applied towards AI governance initiatives worldwide.

Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing. Quantum computing research topics from the Arab world include quantum machine learning and location-tracking and spatial systems. Quantum computing (QC) is one of the most transformative scientific and technological advances of the 21 century, introducing entirely new paradigms for solving computational problems that have long been considered intractable for classical systems. By using the principles of quantum mechanics--superposition, entanglement, and interference--QC has the potential to tackle challenges in fields such as optimization, cryptography, materials science, artificial intelligence, and many others, offering solutions that go beyond the capabilities of conventional computing frameworks. Though the field is still in its developmental stages, progress is being made worldwide, expanding its scope and potential impact.