Seeing Through Risk: A Symbolic Approximation of Prospect Theory

We propose a novel symbolic modeling framework for decision-making under risk that merges interpretability with the core insights of Prospect Theory. Our approach replaces opaque utility curves and probability weighting functions with transparent, effect-size-guided features. We mathematically formalize the method, demonstrate its ability to replicate well-known framing and loss-aversion phenomena, and provide an end-to-end empirical validation on synthetic datasets. The resulting model achieves competitive predictive performance while yielding clear coefficients mapped onto psychological constructs, making it suitable for applications ranging from AI safety to economic policy analysis.

Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Images

This paper introduces the Global-Local Image Perceptual Score (GLIPS), an image metric designed to assess the photorealistic image quality of AI-generated images with a high degree of alignment to human visual perception. Traditional metrics such as FID and KID scores do not align closely with human evaluations. The proposed metric incorporates advanced transformer-based attention mechanisms to assess local similarity and Maximum Mean Discrepancy (MMD) to evaluate global distributional similarity. To evaluate the performance of GLIPS, we conducted a human study on photorealistic image quality. Comprehensive tests across various generative models demonstrate that GLIPS consistently outperforms existing metrics like FID, SSIM, and MS-SSIM in terms of correlation with human scores. Additionally, we introduce the Interpolative Binning Scale (IBS), a refined scaling method that enhances the interpretability of metric scores by aligning them more closely with human evaluative standards. The proposed metric and scaling approach not only provides more reliable assessments of AI-generated images but also suggest pathways for future enhancements in image generation technologies.

Large Language Models Spot Phishing Emails with Surprising Accuracy: A Comparative Analysis of Performance

Phishing, a prevalent cybercrime tactic for decades, remains a significant threat in today's digital world. By leveraging clever social engineering elements and modern technology, cybercrime targets many individuals, businesses, and organizations to exploit trust and security. These cyber-attackers are often disguised in many trustworthy forms to appear as legitimate sources. By cleverly using psychological elements like urgency, fear, social proof, and other manipulative strategies, phishers can lure individuals into revealing sensitive and personalized information. Building on this pervasive issue within modern technology, this paper aims to analyze the effectiveness of 15 Large Language Models (LLMs) in detecting phishing attempts, specifically focusing on a randomized set of "419 Scam" emails. The objective is to determine which LLMs can accurately detect phishing emails by analyzing a text file containing email metadata based on predefined criteria. The experiment concluded that the following models, ChatGPT 3.5, GPT-3.5-Turbo-Instruct, and ChatGPT, were the most effective in detecting phishing emails.