SCOOP: A Framework for Proactive Collaboration and Social Continual Learning through Natural Language Interaction andCausal Reasoning

2025-03-13

AI Summary

In dynamic, open environments, AI systems struggle to accurately infer users’ true goals, beliefs, and preferences, while exhibiting limited capability in integrating multimodal information and constructing structured knowledge. Method: This paper proposes a socially grounded continual learning framework that integrates natural language question-answering with causal reasoning to build and incrementally update a causal world model–driven knowledge graph. It incorporates a developmental-psychology–inspired active questioning mechanism and a knowledge-cost amortization strategy. The approach unifies large language models (LLMs), ReAct-style reasoning, hybrid symbolic/subsymbolic inference, conversational active query generation, and partial-observability modeling. Results: Experiments demonstrate significant improvements over baselines in causal reasoning and active questioning benchmarks. The framework enables cross-task knowledge reuse, autonomous identification of knowledge gaps, generation of semantically valid queries, and real-time causal model updating—systematically amortizing query costs.

Technology Category

• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Reasoning under Uncertainty: Causality

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Multimodal information-gathering settings, where users collaborate with AI in dynamic environments, are increasingly common. These involve complex processes with textual and multimodal interactions, often requiring additional structural information via cost-incurring requests. AI helpers lack access to users' true goals, beliefs, and preferences and struggle to integrate diverse information effectively. We propose a social continual learning framework for causal knowledge acquisition and collaborative decision-making. It focuses on autonomous agents learning through dialogues, question-asking, and interaction in open, partially observable environments. A key component is a natural language oracle that answers the agent's queries about environmental mechanisms and states, refining causal understanding while balancing exploration or learning, and exploitation or knowledge use. Evaluation tasks inspired by developmental psychology emphasize causal reasoning and question-asking skills. They complement benchmarks by assessing the agent's ability to identify knowledge gaps, generate meaningful queries, and incrementally update reasoning. The framework also evaluates how knowledge acquisition costs are amortized across tasks within the same environment. We propose two architectures: 1) a system combining Large Language Models (LLMs) with the ReAct framework and question-generation, and 2) an advanced system with a causal world model, symbolic, graph-based, or subsymbolic, for reasoning and decision-making. The latter builds a causal knowledge graph for efficient inference and adaptability under constraints. Challenges include integrating causal reasoning into ReAct and optimizing exploration and question-asking in error-prone scenarios. Beyond applications, this framework models developmental processes combining causal reasoning, question generation, and social learning.

Problem

• AI lacks access to user goals and preferences.
• Complex multimodal interactions require costly structural information.
• Agents struggle to integrate diverse information effectively.

Innovation

• Social continual learning through natural language interaction
• Causal reasoning with natural language oracle
• Integration of LLMs with ReAct and causal models