The article presents Counterfactual Concept Bottleneck Models (CF-CBMs), a new class of models in deep learning. These models are designed to address three fundamental queries: predicting class labels, explaining task…
Continue readingThis paper discusses the development of an automated system to help people with cognitive disabilities, such as autism, manage transition plans. The system uses a multi-agent planning approach to generate…
Continue readingThe research paper presents a new framework called ‘Formal-LLM’ that integrates the expressiveness of natural language with the precision of formal language to control the plan generation of Large Language…
Continue readingThis research investigates the use of large language models (LLMs) in software penetration testing (pentesting) to identify security vulnerabilities in source code. The authors hypothesize that an LLM-based AI agent…
Continue readingThis paper introduces Investigate-Consolidate-Exploit (ICE), a new approach for improving the adaptability and flexibility of AI agents through inter-task self-evolution. Unlike existing methods that focus on learning within tasks, ICE…
Continue readingThe article discusses reinforcement learning (RL) in multi-agent systems (MAS), focusing on innate-values-driven behaviors of AI agents. It introduces a hierarchical compound intrinsic value reinforcement learning model, termed IVRL, to…
Continue readingThe paper presents XUAT-Copilot, a multi-agent collaborative system developed to automate the User Acceptance Testing (UAT) process of WeChat Pay, a leading mobile payment app in China. The system aims…
Continue readingThe study by Kentaro Mitsui, Yukiya Hono, and Kei Sawada presents CHATS – a system for generating spoken dialogues from written ones between AI agents. Large Language Models (LLMs) have…
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