Papers
Research papers from arXiv and related sources
Language Models Exhibit Inconsistent Biases Towards Algorithmic Agents and Human Experts
Large language models are increasingly used in decision-making tasks that require them to process information from a variety of sources, including both human experts and other algorithmic agents. H...
Jessica Y. Bo, Lillio Mok, Ashton Anderson
Semantic Partial Grounding via LLMs
Grounding is a critical step in classical planning, yet it often becomes a computational bottleneck due to the exponential growth in grounded actions and atoms as task size increases. Recent advanc...
Giuseppe Canonaco, Alberto Pozanco, Daniel Borrajo
RGB-Event HyperGraph Prompt for Kilometer Marker Recognition based on Pre-trained Foundation Models
Metro trains often operate in highly complex environments, characterized by illumination variations, high-speed motion, and adverse weather conditions. These factors pose significant challenges for...
Xiaoyu Xian, Shiao Wang, Xiao Wang, Daxin Tian, Yan Tian
Detecting UX smells in Visual Studio Code using LLMs
Integrated Development Environments shape developers' daily experience, yet the empirical study of their usability and user experience (UX) remains limited. This work presents an LLM-assisted appro...
Andrés Rodriguez, Juan Cruz Gardey, Alejandra Garrido
IOAgent: Democratizing Trustworthy HPC I/O Performance Diagnosis Capability via LLMs
As the complexity of the HPC storage stack rapidly grows, domain scientists face increasing challenges in effectively utilizing HPC storage systems to achieve their desired I/O performance. To iden...
Chris Egersdoerfer, Arnav Sareen, Jean Luca Bez, Suren Byna, Dongkuan, Xu, Dong Dai
Are Foundation Models the Route to Full-Stack Transfer in Robotics?
In humans and robots alike, transfer learning occurs at different levels of abstraction, from high-level linguistic transfer to low-level transfer of motor skills. In this article, we provide an ov...
Freek Stulp, Samuel Bustamante, João Silvério, Alin Albu-Schäffer, Jeannette Bohg, Shuran Song
The Governance of Intimacy: A Preliminary Policy Analysis of Romantic AI Platforms
Romantic AI platforms invite intimate emotional disclosure, yet their data governance practices remain underexamined. This preliminary study analyses the Privacy Policies and Terms of Service of si...
Xiao Zhan, Yifan Xu, Rongjun Ma, Shijing He, Jose Luis Martin-Navarro, Jose Such
Enhancing LLM-Based Test Generation by Eliminating Covered Code
Automated test generation is essential for software quality assurance, with coverage rate serving as a key metric to ensure thorough testing. Recent advancements in Large Language Models (LLMs) hav...
WeiZhe Xu, Mengyu Liu, Fanxin Kong
CxMP: A Linguistic Minimal-Pair Benchmark for Evaluating Constructional Understanding in Language Models
Recent work has examined language models from a linguistic perspective to better understand how they acquire language. Most existing benchmarks focus on judging grammatical acceptability, whereas t...
Miyu Oba, Saku Sugawara
When LoRA Betrays: Backdooring Text-to-Image Models by Masquerading as Benign Adapters
Low-Rank Adaptation (LoRA) has emerged as a leading technique for efficiently fine-tuning text-to-image diffusion models, and its widespread adoption on open-source platforms has fostered a vibrant...
Liangwei Lyu, Jiaqi Xu, Jianwei Ding, Qiyao Deng
Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions
To reduce carbon emissions and minimize shipping costs, improving the fuel efficiency of ships is crucial. Various measures are taken to reduce the total fuel consumption of ships, including optimi...
Dusica Marijan, Hamza Haruna Mohammed, Bakht Zaman
RADAR: Reasoning as Discrimination with Aligned Representations for LLM-based Knowledge Graph Reasoning
Knowledge graph reasoning (KGR) infers missing facts, with recent advances increasingly harnessing the semantic priors and reasoning abilities of Large Language Models (LLMs). However, prevailing g...
Bo Xue, Yuan Jin, Luoyi Fu, Jiaxin Ding, Xinbing Wang
Large Language Models are Algorithmically Blind
Large language models (LLMs) demonstrate remarkable breadth of knowledge, yet their ability to reason about computational processes remains poorly understood. Closing this gap matters for practitio...
Sohan Venkatesh, Ashish Mahendran Kurapath, Tejas Melkote
Hidden Topics: Measuring Sensitive AI Beliefs with List Experiments
How can researchers identify beliefs that large language models (LLMs) hide? As LLMs become more sophisticated and the prevalence of alignment faking increases, combined with their growing integrat...
Maxim Chupilkin
Small Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text
Sarcasm detection in multilingual and code-mixed environments remains a challenging task for natural language processing models due to structural variations, informal expressions, and low-resource ...
Bitan Majumder, Anirban Sen
EmoOmni: Bridging Emotional Understanding and Expression in Omni-Modal LLMs
The evolution of Omni-Modal Large Language Models~(Omni-LLMs) has revolutionized human--computer interaction, enabling unified audio-visual perception and speech response. However, existing Omni-LL...
Wenjie Tian, Zhixian Zhao, Jingbin Hu, Huakang Chen, Haohe Liu, Binshen Mu, Lei Xie
A task-based data-flow methodology for programming heterogeneous systems with multiple accelerator APIs
Heterogeneous nodes that combine multi-core CPUs with diverse accelerators are rapidly becoming the norm in both high-performance computing (HPC) and AI infrastructures. Exploiting these platforms,...
Aleix Boné, Alejandro Aguirre, David Álvarez, Pedro J. Martinez-Ferrer, Vicenç Beltran
2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support
Across a growing number of fields, human decision making is supported by predictions from AI models. However, we still lack a deep understanding of the effects of adoption of these technologies. In...
Otto Nyberg, Fausto Carcassi, Giovanni Cinà
ExpLang: Improved Exploration and Exploitation in LLM Reasoning with On-Policy Thinking Language Selection
Current large reasoning models (LRMs) have shown strong ability on challenging tasks after reinforcement learning (RL) based post-training. However, previous work mainly focuses on English reasonin...
Changjiang Gao, Zixian Huang, Kaichen Yang, Jiajun Chen, Jixing Li, Shujian Huang
Personalized Graph-Empowered Large Language Model for Proactive Information Access
Since individuals may struggle to recall all life details and often confuse events, establishing a system to assist users in recalling forgotten experiences is essential. While numerous studies hav...
Chia Cheng Chang, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen