What Makes a Good Natural Language Prompt?

As large language models (LLMs) have progressed towards more human-like and human--AI communications have become prevalent, prompting has emerged as a decisive component. However, there is limited conceptual consensus on what exactly quantifies natural language prompts. We attempt to address this question by conducting a meta-analysis surveying more than 150 prompting-related papers from leading NLP and AI conferences from 2022 to 2025 and blogs. We propose a property- and human-centric framework for evaluating prompt quality, encompassing 21 properties categorized into six dimensions. We then examine how existing studies assess their impact on LLMs, revealing their imbalanced support across models and tasks, and substantial research gaps. Further, we analyze correlations among properties in high-quality natural language prompts, deriving prompting recommendations. We then empirically explore multi-property prompt enhancements in reasoning tasks, observing that single-property enhancements often have the greatest impact. Finally, we discover that instruction-tuning on property-enhanced prompts can result in better reasoning models. Our findings establish a foundation for property-centric prompt evaluation and optimization, bridging the gaps between human--AI communication and opening new prompting research directions.

LLMs Outperform Experts on Challenging Biology Benchmarks

This study systematically evaluates 27 frontier Large Language Models on eight diverse biology benchmarks spanning molecular biology, genetics, cloning, virology, and biosecurity. Models from major AI developers released between November 2022 and April 2025 were assessed through ten independent runs per benchmark. The findings reveal dramatic improvements in biological capabilities. Top model performance increased more than 4-fold on the challenging text-only subset of the Virology Capabilities Test over the study period, with the top model now performing twice as well as expert virologists. Several models now match or exceed expert-level performance on other challenging benchmarks, including LAB-Bench CloningScenarios and the biology subsets of GPQA and WMDP. Contrary to expectations, chain-of-thought did not substantially improve performance over zero-shot evaluation, while extended reasoning features in o3-mini and Claude 3.7 Sonnet typically improved performance as predicted by inference scaling. Benchmarks such as PubMedQA and the MMLU and WMDP biology subsets exhibited performance plateaus well below 100%, suggesting benchmark saturation and errors in the underlying benchmark data. The analysis highlights the need for more sophisticated evaluation methodologies as AI systems continue to advance.

The Growth of E-Bike Use: A Machine Learning Approach

We present our work on electric bicycles (e-bikes) and their implications for policymakers in the United States. E-bikes have gained significant popularity as a fast and eco-friendly transportation option. As we strive for a sustainable energy plan, understanding the growth and impact of e-bikes is crucial for policymakers. Our mathematical modeling offers insights into the value of e-bikes and their role in the future. Using an ARIMA model, a supervised machine-learning algorithm, we predicted the growth of e-bike sales in the U.S. Our model, trained on historical sales data from January 2006 to December 2022, projected sales of 1.3 million units in 2025 and 2.113 million units in 2028. To assess the factors contributing to e-bike usage, we employed a Random Forest regression model. The most significant factors influencing e-bike sales growth were disposable personal income and popularity. Furthermore, we examined the environmental and health impacts of e-bikes. Through Monte Carlo simulations, we estimated the reduction in carbon emissions due to e-bike use and the calories burned through e-biking. Our findings revealed that e-bike usage in the U.S. resulted in a reduction of 15,737.82 kilograms of CO2 emissions in 2022. Additionally, e-bike users burned approximately 716,630.727 kilocalories through their activities in the same year. Our research provides valuable insights for policymakers, emphasizing the potential of e-bikes as a sustainable transportation solution. By understanding the growth factors and quantifying the environmental and health benefits, policymakers can make informed decisions about integrating e-bikes into future energy and transportation strategies.

DBLPLink 2.0 -- An Entity Linker for the DBLP Scholarly Knowledge Graph

In this work we present an entity linker for DBLP's 2025 version of RDF-based Knowledge Graph. Compared to the 2022 version, DBLP now considers publication venues as a new entity type called dblp:Stream. In the earlier version of DBLPLink, we trained KG-embeddings and re-rankers on a dataset to produce entity linkings. In contrast, in this work, we develop a zero-shot entity linker using LLMs using a novel method, where we re-rank candidate entities based on the log-probabilities of the "yes" token output at the penultimate layer of the LLM.

Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms

We consider the problem of minimizing a difference of (smooth) convex functions over a compact convex feasible region $P$, i.e., $\min_{x \in P} f(x) - g(x)$, with smooth $f$ and Lipschitz continuous $g$. This computational study builds upon and complements the framework of Maskan et al. [2025] by integrating advanced Frank-Wolfe variants to reduce computational overhead. We empirically show that constrained DC problems can be efficiently solved using a combination of the Blended Pairwise Conditional Gradients (BPCG) algorithm [Tsuji et al., 2022] with warm-starting and the adaptive error bound from Maskan et al. [2025]. The result is a highly efficient and scalable projection-free algorithm for constrained DC optimization.

Shifting the Lens: Detecting Malware in npm Ecosystem with Large Language Models

The Gartner 2022 report predicts that 45% of organizations worldwide will encounter software supply chain attacks by 2025, highlighting the urgency to improve software supply chain security for community and national interests. Current malware detection techniques aid in the manual review process by filtering benign and malware packages, yet such techniques have high false-positive rates and limited automation support. Therefore, malware detection techniques could benefit from advanced, more automated approaches for accurate and minimally false-positive results. The goal of this study is to assist security analysts in identifying malicious packages through the empirical study of large language models (LLMs) to detect potential malware in the npm ecosystem. We present SocketAI Scanner, a multi-stage decision-maker malware detection workflow using iterative self-refinement and zero-shot-role-play-Chain of Thought (CoT) prompting techniques for ChatGPT. We studied 5,115 npm packages (of which 2,180 are malicious) and performed a baseline comparison of the GPT-3 and GPT-4 models with a static analysis tool. Our findings showed promising results for GPT models with low misclassification alert rates. Our baseline comparison demonstrates a notable improvement over static analysis in precision scores above 25% and F1 scores above 15%. We attained precision and F1 scores of 91% and 94%, respectively, for the GPT-3 model. Overall, GPT-4 demonstrates superior performance in precision (99%) and F1 (97%) scores, while GPT-3 presents a cost-effective balance between performance and expenditure.

Applying Machine Learning Tools for Urban Resilience Against Floods

Floods are among the most prevalent and destructive natural disasters, often leading to severe social and economic impacts in urban areas due to the high concentration of assets and population density. In Iran, particularly in Tehran, recurring flood events underscore the urgent need for robust urban resilience strategies. This paper explores flood resilience models to identify the most effective approach for District 6 in Tehran. Through an extensive literature review, various resilience models were analyzed, with the Climate Disaster Resilience Index (CDRI) emerging as the most suitable model for this district due to its comprehensive resilience dimensions: Physical, Social, Economic, Organizational, and Natural Health resilience. Although the CDRI model provides a structured approach to resilience measurement, it remains a static model focused on spatial characteristics and lacks temporal adaptability. An extensive literature review enhances the CDRI model by integrating data from 2013 to 2022 in three-year intervals and applying machine learning techniques to predict resilience dimensions for 2025. This integration enables a dynamic resilience model that can accommodate temporal changes, providing a more adaptable and data driven foundation for urban flood resilience planning. By employing artificial intelligence to reflect evolving urban conditions, this model offers valuable insights for policymakers and urban planners to enhance flood resilience in Tehrans critical District 6.

Hidden Persuasion: Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasion of Ukraine

This paper presents one of the top-performing solutions to the UNLP 2025 Shared Task on Detecting Manipulation in Social Media. The task focuses on detecting and classifying rhetorical and stylistic manipulation techniques used to influence Ukrainian Telegram users. For the classification subtask, we fine-tuned the Gemma 2 language model with LoRA adapters and applied a second-level classifier leveraging meta-features and threshold optimization. For span detection, we employed an XLM-RoBERTa model trained for multi-target, including token binary classification. Our approach achieved 2nd place in classification and 3rd place in span detection.

Comparator-Adaptive $Φ$-Regret: Improved Bounds, Simpler Algorithms, and Applications to Games

In the classic expert problem, $\Phi$-regret measures the gap between the learner's total loss and that achieved by applying the best action transformation $\phi \in \Phi$. A recent work by Lu et al., [2025] introduces an adaptive algorithm whose regret against a comparator $\phi$ depends on a certain sparsity-based complexity measure of $\phi$, (almost) recovering and interpolating optimal bounds for standard regret notions such as external, internal, and swap regret. In this work, we propose a general idea to achieve an even better comparator-adaptive $\Phi$-regret bound via much simpler algorithms compared to Lu et al., [2025]. Specifically, we discover a prior distribution over all possible binary transformations and show that it suffices to achieve prior-dependent regret against these transformations. Then, we propose two concrete and efficient algorithms to achieve so, where the first one learns over multiple copies of a prior-aware variant of the Kernelized MWU algorithm of Farina et al., [2022], and the second one learns over multiple copies of a prior-aware variant of the BM-reduction [Blum and Mansour, 2007]. To further showcase the power of our methods and the advantages over Lu et al., [2025] besides the simplicity and better regret bounds, we also show that our second approach can be extended to the game setting to achieve accelerated and adaptive convergence rate to $\Phi$-equilibria for a class of general-sum games. When specified to the special case of correlated equilibria, our bound improves over the existing ones from Anagnostides et al., [2022a,b]

Artificial Intelligence in Creative Industries: Advances Prior to 2025

The rapid advancements in artificial intelligence (AI), particularly in generative AI and large language models (LLMs), have profoundly impacted the creative industries by enabling innovative content creation, enhancing workflows, and democratizing access to creative tools. This paper explores the significant technological shifts since our previous review in 2022, highlighting how these developments have expanded creative opportunities and efficiency. These technological advancements have enhanced the capabilities of text-to-image, text-to-video, and multimodal generation technologies. In particular, key breakthroughs in LLMs have established new benchmarks in conversational AI, while advancements in image generators have revolutionized content creation. We also discuss AI integration into post-production workflows, which has significantly accelerated and refined traditional processes. Despite these innovations, challenges remain, particularly for the media industry, due to the demands on communication traffic from creative content. We therefore include data compression and quality assessment in this paper. Furthermore, we highlight the trend toward unified AI frameworks capable of addressing multiple creative tasks and underscore the importance of human oversight to mitigate AI-generated inaccuracies. Finally, we explore AI's future potential in the creative sector, stressing the need to navigate emerging challenges to maximize its benefits while addressing associated risks.