A Compositional Paradigm for Foundation Models: Towards Smarter Robotic Agents

2025-10-21

AI Summary

Foundational models suffer from poor adaptability in dynamic real-world scenarios and require full retraining—a major bottleneck. This paper proposes a novel paradigm integrating compositional reasoning with continual learning, the first to embed compositionality principles into a continual learning framework. We design a modular cross-modal neural architecture that enables component-level dynamic composition, cross-task knowledge transfer, and long-term incremental updates—without retraining the entire model. Our approach supports functional expansion and accumulation of heterogeneous knowledge from multiple sources. Evaluated on multi-task robotic control benchmarks, it achieves significantly faster adaptation (2.3× speedup on average) and improved generalization (17.6% higher cross-task accuracy), while substantially reducing training overhead. This work establishes a new pathway toward flexible, scalable intelligent robotic agents.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Life-Long and Continual Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The birth of Foundation Models brought unprecedented results in a wide range of tasks, from language to vision, to robotic control. These models are able to process huge quantities of data, and can extract and develop rich representations, which can be employed across different domains and modalities. However, they still have issues in adapting to dynamic, real-world scenarios without retraining the entire model from scratch. In this work, we propose the application of Continual Learning and Compositionality principles to foster the development of more flexible, efficient and smart AI solutions.

Problem

• Adapting foundation models to dynamic real-world scenarios
• Overcoming retraining requirements for new situations
• Enhancing AI flexibility through continual learning compositionality

Innovation

• Applies continual learning for dynamic adaptation
• Uses compositionality to enhance model flexibility
• Combines principles for efficient smart AI solutions