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Episodic Memory in Agentic Frameworks: Suggesting Next Tasks
November 21, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Sandro Rama Fiorini, Leonardo G. Azevedo, Raphael M. Thiago, Valesca M. de Sousa, Anton B. Labate, Viviane Torres da Silva
arXiv ID
2511.17775
Category
cs.MA: Multiagent Systems
Cross-listed
cs.AI,
cs.LG
Citations
0
Venue
arXiv.org
Last Checked
6 months ago
Abstract
Agentic frameworks powered by Large Language Models (LLMs) can be useful tools in scientific workflows by enabling human-AI co-creation. A key challenge is recommending the next steps during workflow creation without relying solely on LLMs, which risk hallucination and require fine-tuning with scarce proprietary data. We propose an episodic memory architecture that stores and retrieves past workflows to guide agents in suggesting plausible next tasks. By matching current workflows with historical sequences, agents can recommend steps based on prior patterns.
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