mlsanityData to 7 Oct 2026

Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents

arXiv 2602.22523 ↗First posted 26 Feb 2026cs.AI cs.CL q-bio.NCcited by 2

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

While contemporary large language models (LLMs) 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 (AI) 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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 99 of its 217 references that are in the map. First posted on arXiv 26 Feb 2026.

Builds on

99 of 217 references.

Closest ancestors · 11

Same direction, newest first

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  5. 2024Tree Search for Language Model Agents1 Jul
  6. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains17 Jun
  7. 2023FireAct: Toward Language Agent Fine-tuning9 Oct
  8. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models6 Oct
  9. 2023The Rise and Potential of Large Language Model Based Agents: A Survey14 Sep
  10. 2023Cognitive Architectures for Language Agents5 Sep
  11. 2023A Survey on Large Language Model based Autonomous Agents22 Aug

Other references · 88

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  5. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  6. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  7. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  8. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  9. 2021Convex optimizationcited by 2,479
  10. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
  11. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  12. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,037
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  3. 2016Concrete Problems in AI Safetycited by 1,582
  4. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
  5. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,371
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  10. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
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  12. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,002
  13. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agentscited by 956
  14. 2023Reasoning with Language Model is Planning with World Modelcited by 846
  15. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
  16. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 622
  17. 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
  18. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 554
  19. 2024Executable Code Actions Elicit Better LLM Agentscited by 537
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  22. 2017A Tutorial on Thompson Samplingcited by 420
  23. 2024AFlow: Automating Agentic Workflow Generationcited by 395
  24. 2025ReTool: Reinforcement Learning for Strategic Tool Use in LLMscited by 377
  25. 2025Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRscited by 373
  26. 2025Agentic Context Engineering: Evolving Contexts for Self-Improving Language Modelscited by 349
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  29. 2023MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedbackcited by 293
  30. 2019Challenges of Real-World Reinforcement Learningcited by 282
  31. 2023A Survey of Reinforcement Learning from Human Feedbackcited by 259
  32. 2024WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learningcited by 243
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  35. 2014An Information-Theoretic Analysis of Thompson Samplingcited by 178
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  38. 2014Learning to Optimize via Information-Directed Samplingcited by 168

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

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