12 stories tagged Autonomous Agents
Researchers from ArXiv cs.AI introduced ToolDNS, a system that retrofits semantic tool discovery onto the internet's Domain Name System (DNS), enabling AI agents to find tools efficiently at scale without centralized bottlenecks.
Researchers developed a system to prevent safety violations in autonomous agents. It uses five gears to manage different stages of operation, ensuring stability and continuity. This could make AI-driven robots and software agents safer for real-world use.
Researchers developed a new approach called ATOD to improve AI agents for complex tasks. It combines imitation learning with reinforcement learning to help agents learn faster and perform better.
Researchers suggest a new way to govern AI agents by focusing on actions rather than their reasoning. This approach could make AI decisions more transparent and trustworthy in critical areas like healthcare and software deployment.
Researchers developed Heuresis, a framework to help AI agents explore high-quality, diverse, and novel ideas in machine learning. This could speed up scientific progress by making AI research more efficient and creative.
AI systems often act without proper authorization or evidence, a problem called 'compliance bias'. Researchers propose new ways to evaluate when AI should abstain from actions. This could make AI safer and more reliable in real-world use.
Researchers propose a new way for AI to automatically gather and prepare its own specialized training data. This could make AI models better at niche tasks without human help. The approach, called 'Autonomous Agentic Data Engineering,' lets AI act as its own data engineer, potentially speeding up customization for fields like medicine or law.
Researchers introduce MolClaw, an autonomous AI agent that excels in drug molecule evaluation, screening, and optimization. The agent uses a three-tier hierarchical skill architecture to unify over 30 specialized tools, addressing key challenges in computational drug discovery.
Researchers introduce Memanto, a new memory system for autonomous agents that uses typed semantic storage and information-theoretic retrieval. This approach aims to reduce computational overhead compared to traditional graph-based methods.
Researchers have developed an autonomous LLM agent that can independently formulate, test, and refine materials science theories. The agent successfully replicated established equations like the Hall-Petch equation and Paris law without human intervention.
Hyperframes is a new tool enabling autonomous agents to create and edit videos. It allows agents to generate videos from text, images, or other videos, streamlining content creation.
Researchers introduce WebXSkill, a framework that combines executable skills with natural language understanding to improve autonomous web agents. This innovation addresses the grounding gap in current LLM-powered agents, enhancing their ability to complete complex browser tasks.