Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents
#Language Agents #Cognitive Models #AI Algorithms #Large Language Models #Modular Systems #arXiv #Multi-Agent AI
📌 Key Takeaways
- Researchers propose using cognitive models and AI algorithms as templates for designing modular language agents
- The paper formalizes the concept of 'agent templates' for combining multiple LLMs
- Existing language agents in literature already use templates derived from cognitive models and AI algorithms
- These designs can lead to more effective and interpretable language agents
📖 Full Retelling
Researchers Ryan Liu and five co-authors from various institutions introduced a new position paper on arXiv on February 26, 2026, proposing that cognitive models and AI algorithms can serve as templates for designing modular language agents to solve complex problems beyond the capabilities of single large language models (LLMs). The paper addresses a significant challenge in artificial intelligence: while contemporary LLMs have become increasingly capable when operating independently, there remain numerous difficult problems that exceed the capacity of any single model. The authors formalize the concept of 'agent templates' that specify roles for individual LLMs and outline how their functionalities should be composed to create more powerful systems. By surveying existing language agents in the literature, they highlight underlying templates derived directly from cognitive models or AI algorithms, demonstrating how these designs can lead to more effective and interpretable language agents. The research contributes to the growing field of multi-agent AI systems by providing a theoretical framework for combining specialized LLMs into coordinated systems that can tackle increasingly complex tasks.
🏷️ Themes
Artificial Intelligence, Cognitive Science, Multi-Agent Systems
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Entity Intersection Graph
Connections for Large language model:
🌐
Educational technology
4 shared
🌐
Reinforcement learning
3 shared
🌐
Machine learning
2 shared
🌐
Artificial intelligence
2 shared
🌐
Benchmark
2 shared
Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.22523 [Submitted on 26 Feb 2026] Title: Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents Authors: Ryan Liu , Dilip Arumugam , Cedegao E. Zhang , Sean Escola , Xaq Pitkow , Thomas L. Griffiths View a PDF of the paper titled Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents, by Ryan Liu and 5 other authors View PDF HTML Abstract: While contemporary large language models are increasingly capable in isolation, there are still many difficult problems that lie beyond the abilities of a single LLM. For such tasks, there is still uncertainty about how best to take many LLMs as parts and combine them into a greater whole. This position paper argues that potential blueprints for designing such modular language agents can be found in the existing literature on cognitive models and artificial intelligence algorithms. To make this point clear, we formalize the idea of an agent template that specifies roles for individual LLMs and how their functionalities should be composed. We then survey a variety of existing language agents in the literature and highlight their underlying templates derived directly from cognitive models or AI algorithms. By highlighting these designs, we aim to call attention to agent templates inspired by cognitive science and AI as a powerful tool for developing effective, interpretable language agents. Subjects: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Neurons and Cognition (q-bio.NC) Cite as: arXiv:2602.22523 [cs.AI] (or arXiv:2602.22523v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.22523 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Cedegao Zhang [ view email ] [v1] Thu, 26 Feb 2026 01:35:32 UTC (67 KB) Full-text links: Access Paper: View a PDF of the paper titled Cognitive Models and AI Algorithms Provide Templates for Designing Langua...
Read full article at source