How can LLMs collaborate better via causal reasoning and question-asking?
SCOOP: A Framework for Proactive Collaboration and Social Continual Learning through Natural Language Interaction and Causal Reasoning
March 14, 2025
https://arxiv.org/pdf/2503.10241This paper introduces SCOOP, a framework for building multi-agent AI systems that can learn and make decisions in complex, dynamic environments through natural language interaction and causal reasoning. It focuses on agents that can proactively gather information by asking questions (like humans) and learn continually from new experiences.
Key points for LLM-based multi-agent systems:
- Combines LLMs with Causal Reasoning: SCOOP integrates the strengths of LLMs (natural language understanding, generation) with structured causal reasoning to improve decision-making. This involves representing knowledge in causal graphs, which can be updated through interaction with a "natural language oracle."
- Focus on Question-Asking: SCOOP emphasizes the importance of agents actively seeking information by asking questions, both to users and to a knowledge source (the oracle). This addresses the limitation of LLMs relying solely on provided information.
- Continual Learning: The framework is designed for agents to learn continually from multiple tasks within the same environment, amortizing the cost of acquiring knowledge. This makes it suitable for dynamic, real-world scenarios.
- ReAct Framework Integration: SCOOP builds upon the ReAct framework (Reason + Act), extending it with causal reasoning capabilities and the ability to query for information.
- Relevance to Web Development: Although not explicitly stated, the focus on natural language interaction and dynamic environments makes this framework potentially applicable to building interactive, intelligent web applications where agents can learn and adapt to user needs. Think of complex web apps that guide users through processes (like applying for a visa) or assist with tasks involving external information retrieval and integration.