arXiv:2512.20328v1 Announce Type: cross Abstract: Recent progress in Large Language Models (LLMs) has substantially advanced the automation of software engineering (SE) tasks, enabling complex activities such as code generation and code summarization. However, the black-box nature of LLMs remains a major barrier to their adoption in high-stakes and safety-critical domains, where explainability and transparency are […]
Multi-LLM Thematic Analysis with Dual Reliability Metrics: Combining Cohen’s Kappa and Semantic Similarity for Qualitative Research Validation
arXiv:2512.20352v1 Announce Type: cross Abstract: Qualitative research faces a critical reliability challenge: traditional inter-rater agreement methods require multiple human coders, are time-intensive, and often yield moderate consistency. We present a multi-perspective validation framework for LLM-based thematic analysis that combines ensemble validation with dual reliability metrics: Cohen’s Kappa ($kappa$) for inter-rater agreement and cosine similarity for […]
LeLaR: The First In-Orbit Demonstration of an AI-Based Satellite Attitude Controller
arXiv:2512.19576v2 Announce Type: replace-cross Abstract: Attitude control is essential for many satellite missions. Classical controllers, however, are time-consuming to design and sensitive to model uncertainties and variations in operational boundary conditions. Deep Reinforcement Learning (DRL) offers a promising alternative by learning adaptive control strategies through autonomous interaction with a simulation environment. Overcoming the Sim2Real gap, […]
Allelopathy of Rumex spp.: A review
arXiv:2512.19762v1 Announce Type: new Abstract: The genus of Rumex from the Polygonaceae family is widespread in the world, particularly in the northern hemisphere, and includes about 250 species. The species of this genus are used for medicinal purposes and their allelopathic impacts. Regarding allelopathy, many allelochemicals have been detected in different Rumex species. Therefore, plant […]
VTCBench: Can Vision-Language Models Understand Long Context with Vision-Text Compression?
arXiv:2512.15649v2 Announce Type: replace-cross Abstract: The computational and memory overheads associated with expanding the context window of LLMs severely limit their scalability. A noteworthy solution is vision-text compression (VTC), exemplified by frameworks like DeepSeek-OCR and Glyph, which convert long texts into dense 2D visual representations, thereby achieving token compression ratios of 3x-20x. However, the impact […]
TableGPT-R1: Advancing Tabular Reasoning Through Reinforcement Learning
arXiv:2512.20312v1 Announce Type: cross Abstract: Tabular data serves as the backbone of modern data analysis and scientific research. While Large Language Models (LLMs) fine-tuned via Supervised Fine-Tuning (SFT) have significantly improved natural language interaction with such structured data, they often fall short in handling the complex, multi-step reasoning and robust code execution required for real-world […]
SlideTailor: Personalized Presentation Slide Generation for Scientific Papers
arXiv:2512.20292v1 Announce Type: cross Abstract: Automatic presentation slide generation can greatly streamline content creation. However, since preferences of each user may vary, existing under-specified formulations often lead to suboptimal results that fail to align with individual user needs. We introduce a novel task that conditions paper-to-slides generation on user-specified preferences. We propose a human behavior-inspired […]
Social Comparison without Explicit Inference of Others’ Reward Values: A Constructive Approach Using a Probabilistic Generative Model
arXiv:2512.18687v2 Announce Type: replace Abstract: Social comparison$unicodex2014$the process of evaluating one’s rewards relative to others$unicodex2014$plays a fundamental role in primate social cognition. However, it remains unknown from a computational perspective how information about others’ rewards affects the evaluation of one’s own reward. With a constructive approach, this study examines whether monkeys merely recognize objective reward […]
Retrieval-augmented Prompt Learning for Pre-trained Foundation Models
arXiv:2512.20145v1 Announce Type: cross Abstract: The pre-trained foundation models (PFMs) have become essential for facilitating large-scale multimodal learning. Researchers have effectively employed the “pre-train, prompt, and predict” paradigm through prompt learning to induce improved few-shot performance. However, prompt learning approaches for PFMs still follow a parametric learning paradigm. As such, the stability of generalization in […]
Interaction Dataset of Autonomous Vehicles with Traffic Lights and Signs
arXiv:2501.12536v2 Announce Type: replace-cross Abstract: This paper presents the development of a comprehensive dataset capturing interactions between Autonomous Vehicles (AVs) and traffic control devices, specifically traffic lights and stop signs. Derived from the Waymo Motion dataset, our work addresses a critical gap in the existing literature by providing real-world trajectory data on how AVs navigate […]