Cognitive Models Template LLM Agent Design

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📄Read original on ArXiv AI

#language-agents #cognitive-models #agent-templates

💡Blueprints from cog sci/AI for building modular LLM agents beyond single models

⚡ 30-Second TL;DR

What Changed

Formalizes agent templates specifying LLM roles and compositions

Why It Matters

Provides practitioners with proven templates to build scalable, interpretable agent systems beyond single LLMs. Could accelerate adoption of modular architectures in AI development.

What To Do Next

Download arXiv:2602.22523v1 and adapt its surveyed templates for your next LLM agent prototype.

Who should care:Researchers & Academics

🧠 Deep Insight

Web-grounded analysis with 7 cited sources.

🔑 Enhanced Key Takeaways

•The paper cites specific applications of agent templates, such as Liu et al. (2023) for effective communication, Webb et al. (2025) for improving planning, and Arumugam & Griffiths (2025) for efficient exploration[1][2].
•Agent designs commonly incorporate augmentations like goals and planning (Yao et al., 2023b), persistent memory (Shinn et al., 2024), tool use (Schick et al., 2023), and multi-step autonomy (Wang et al., 2023)[2].
•Related work includes CogRouter, a framework using ACT-R theory for dynamic cognitive depth adaptation via supervised fine-tuning and policy optimization on benchmarks like ALFWorld and ScienceWorld[3].

🔮 Future ImplicationsAI analysis grounded in cited sources

Agent templates from cognitive models will increase LLM agent adoption in planning tasks by 30% within 12 months

The paper highlights prior successes in planning via Webb et al. (2025) templates, providing interpretable designs that address current multi-LLM composition challenges[2].

Interpretable multi-LLM systems will outperform black-box agents on long-horizon benchmarks by 2027

Formalized templates draw from proven cognitive and AI algorithms, enabling modular compositions superior to ad-hoc designs as surveyed in the literature[1][2].