- The paper analyzes applying cognitive design patterns from traditional AI architectures (like observe-decide-act, memory) to Agentic LLM systems, providing a framework for AGI development.
- It identifies successful cognitive patterns in current LLM agents like ReAct (observe-decide-act cycle) and Generative Agents (episodic memory), detailing how these enhance reasoning and action.
- The research highlights opportunities for integrating underrepresented patterns like knowledge compilation and reconsideration, alongside novel patterns like step-wise reflection, to advance LLM agent capabilities and accelerate AGI progress.
Architectural Precedents for General Agents Using LLMs
The paper, "Architectural Precedents for General Agents using LLMs," explores the intersection of cognitive architecture and the deployment of Agentic systems supported by LLMs. This research is rooted in AI and AGI, focusing on the mechanisms and representations necessary for realizing artificial general intelligence. The authors put forth a comparative analysis of cognitive design patterns and their applicability in current LLM systems, especially those designed for interaction and reasoning.
Cognitive architectures traditionally encompass theories and commitments toward systems architectures that define general intelligence. Despite the diversity in sources, a convergence around a high-level cognitive architecture is noteworthy. The authors posit that identifying these design patterns facilitates an informed trajectory toward AGI. The introduction of LLMs presents a new hybrid architecture combining these cognitive patterns with modern AI techniques, where LLMs are not standalone solutions, but integral components within broader AI systems architecture.
The paper systematically categorizes cognitive design patterns that are recurrent in pre-transformer AI systems and explores their effectiveness when integrated with LLMs. These patterns include "observe-decide-act," hierarchical decomposition, memory designations, and procedural knowledge representations. The framework laid out by the authors is exemplary in understanding existing gaps and predicting future research directions for AGI.
Cognitive Design Patterns in Agentic LLM Systems
The authors argue that cognitive design patterns are crucial for structuring the analysis and development of Agentic LLM systems. These design patterns provide abstract solutions to specific challenges inherent in creating intelligent agents. The analysis uncovers existing patterns in Agentic LLM systems like ReAct and generative agents.
One significant cognitive design pattern identified is the "Observe-Decide-Act" cycle, prominently seen in ReAct systems. This pattern delineates reasoning steps distinctively, allowing LLMs to reason before acting, thereby enhancing decision-making processes. The authors highlight that while ReAct shares features with the traditional BDI architecture, additional research in commitment and reconsideration processes could further optimize this pattern in LLM systems.
Episodic memory is another pattern explored, demonstrated through Generative Agents. The paper presents this as a vital pattern, enabling agents to incorporate historical knowledge effectively to influence future actions. The detailed comparison with established characteristics of episodic memory, as seen in cognitive architectures, offers insights into its application in enhancing LLM systems.
Opportunities and Future Directions
The paper anticipates further exploration of cognitive design patterns, like knowledge compilation and reconsideration, that are underrepresented in current LLM research. Knowledge compilation, which deals with learning through caching computational reasoning for future use, is highlighted as a promising direction to reduce computational overhead in agentic systems. Similarly, implementing reconsideration processes in LLM systems could enable dynamic and adaptive decision-making, a staple for achieving AGI.
The authors propose consideration of novel cognitive design patterns unique to Agentic LLM systems due to their different operational paradigms compared to traditional architectures. Step-wise reflection, which improves the reliability of responses, is detailed as one such pattern, combining aspects of reflection and self-assessment, indicative of LLM's potential to redefine cognitive design patterns through novel innovations.
Conclusion
The research presented in the paper fundamentally offers a structured approach to leveraging cognitive design patterns in Agentic LLM systems, furnishing a roadmap towards AGI. By analyzing and co-opting patterns from traditional cognitive architectures, the paper accentuates potential pathways for expanding the scope and efficacy of LLM systems in general intelligence tasks. Use of cognitive design patterns can accelerate research across divergent fields, as researchers strive to build architectures with enriched AI capabilities, bridging the gap towards AGI. Overall, this paper promotes a comparative and integrative methodology, encouraging the broader AI community to consider cognitive design patterns as a cornerstone of future research endeavors.