Prospective Multimodal Memory Consolidation for Long-Horizon Vision-Language Agent Planning
Keywords:
Multimodal Consolidation, Vision-Language Agents, Long-Horizon Planning, Prospective MemoryAbstract
The integration of visual perception and natural language processing has significantly advanced the capabilities of artificial agents, yet long-horizon planning remains a formidable challenge due to the temporal degradation of contextual information and the sheer volume of multimodal data. This paper proposes a novel framework centered on prospective multimodal memory consolidation to address these limitations in vision-language agent planning. By mimicking human cognitive processes wherein prospective memory allows for the anticipation of future states based on consolidated past experiences, the proposed architecture dynamically filters, abstracts, and restructures high-dimensional multimodal inputs. This process ensures that agents retain semantically relevant features while discarding redundant perceptual noise over extended operational timeframes. Through a systematic evaluation across diverse simulated environments demanding complex multi-step reasoning, we demonstrate that prospective memory consolidation substantially mitigates the catastrophic forgetting commonly observed in standard transformer-based memory buffers. The methodology introduces a temporal abstraction mechanism and cross-modal alignment protocols that synergize to optimize long-term planning trajectories. Comprehensive experimental results indicate significant improvements in both task success rates and computational efficiency compared to contemporary baselines. Ultimately, this research provides a robust theoretical and empirical foundation for developing autonomous systems capable of sustained, goal-directed behavior in dynamic and unstructured environments.
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