newsResearchers introduce PseudoAct, a new framework that uses pseudocode synthesis to improve large language model agent planning and action control. The method achieves significant performance improvements over existing reactive approaches, with a 20.93% absolute gain in success rate on FEVER benchmark and new state-of-the-art results on HotpotQA.
Researchers introduced VAF, a systematic evaluation pipeline to measure how visual web elements influence AI agent decision-making. The study tested 48 variants across 5 real-world websites and found that background contrast, item size, position, and card clarity significantly impact agent behavior, while font styling and text color have minimal effects.
Researchers have introduced fEDM+, an enhanced fuzzy ethical decision-making framework for AI systems that provides principle-level explainability and validates decisions against multiple stakeholder perspectives. The framework extends the original fEDM by adding transparent explanations of ethical decisions and replacing single-point validation with pluralistic validation that accommodates different ethical viewpoints.
Researchers have developed ArgLLM-App, a web-based system that uses Large Language Models for argumentative reasoning in decision-making tasks. The system allows human users to visualize explanations and contest reasoning mistakes, making AI decisions more transparent and contestable.
Researchers introduce resilient strategies for stochastic systems, focusing on decision-making that remains robust against disturbances that could flip agent decisions. The work presents fundamental problems for Markov decision processes with reachability and safety objectives, extending to stochastic games with various disturbance aggregation methods.