AutoRAS: Learning Robust Agentic Systems with Primitive Representations
📰 ArXiv cs.AI
Learn to design robust agentic systems using AutoRAS, a framework for automated design with primitive representations, to improve large language models' performance and robustness
Action Steps
- Implement AutoRAS framework using Python and TensorFlow to design robust agentic systems
- Use primitive representations to encode agent behaviors and interactions
- Train and evaluate the robustness of the designed systems using simulated environments and adversarial testing
- Apply AutoRAS to real-world applications, such as language models and autonomous systems
- Compare the performance and robustness of AutoRAS-designed systems with traditional handcrafted approaches
Who Needs to Know This
AI researchers and engineers working on large language models and multi-agent systems can benefit from this framework to improve the robustness and scalability of their models
Key Insight
💡 AutoRAS framework can improve the robustness and scalability of large language models by automating the design of agentic systems with primitive representations
Share This
🤖 Introducing AutoRAS: a framework for automated design of robust agentic systems with primitive representations #AI #LLMs #MultiAgentSystems
Full Article
Title: AutoRAS: Learning Robust Agentic Systems with Primitive Representations
Abstract:
arXiv:2606.21445v1 Announce Type: new Abstract: The automated design of agentic systems offers a promising pathway for scaling large language models (LLMs) beyond single-agent reasoning. While prior work has advanced task performance through handcrafted or automatically generated multi-agent workflows, robustness is often treated as an afterthought, leaving systems vulnerable to external adversaries and internal failures. We propose AutoRAS, a framework for the Automated design of Robust Agentic
Abstract:
arXiv:2606.21445v1 Announce Type: new Abstract: The automated design of agentic systems offers a promising pathway for scaling large language models (LLMs) beyond single-agent reasoning. While prior work has advanced task performance through handcrafted or automatically generated multi-agent workflows, robustness is often treated as an afterthought, leaving systems vulnerable to external adversaries and internal failures. We propose AutoRAS, a framework for the Automated design of Robust Agentic
DeepCamp AI