# Agents Thinking Fast and Slow: A Talker-Reasoner Architecture

arXiv: https://arxiv.org/abs/2410.08328 · HTML: https://mlsanity.com/paper/2410.08328/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 29 of its 40 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [Reasoning with Language Model is Planning with World Model](https://mlsanity.com/paper/2305.14992/) (2023)
- [Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents](https://mlsanity.com/paper/2302.01560/) (2023)
- [Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions](https://mlsanity.com/paper/2306.02224/) (2023)
- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [Self-Discover: Large Language Models Self-Compose Reasoning Structures](https://mlsanity.com/paper/2402.03620/) (2024)
- [Large Language Models as Generalizable Policies for Embodied Tasks](https://mlsanity.com/paper/2310.17722/) (2023)
- [How FaR Are Large Language Models From Agents with Theory-of-Mind?](https://mlsanity.com/paper/2310.03051/) (2023)
- [Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning](https://mlsanity.com/paper/2312.05230/) (2023)
- [LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning](https://mlsanity.com/paper/2401.00125/) (2023)
- [Learning Actionable Representations with Goal-Conditioned Policies](https://mlsanity.com/paper/1811.07819/) (2018)
- [Large Language Models can Implement Policy Iteration](https://mlsanity.com/paper/2210.03821/) (2022)
- [Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks](https://mlsanity.com/paper/2312.06876/) (2023)
- [Empowering Large Language Model Agents through Action Learning](https://mlsanity.com/paper/2402.15809/) (2024)

## Built on by (most cited first, 30 of 36)

- [Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models](https://mlsanity.com/paper/2505.04921/) (2025)
- [A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents](https://mlsanity.com/paper/2602.06052/) (2026)
- [A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law](https://mlsanity.com/paper/2505.02665/) (2025)
- [SOLAMI: Social Vision-Language-Action Modeling for Immersive Interaction with 3D Autonomous Characters](https://mlsanity.com/paper/2412.00174/) (2024)
- [A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications](https://mlsanity.com/paper/2605.07358/) (2026)
- [Enhancing LLM Reasoning with Multi-Path Collaborative Reactive and Reflection agents](https://mlsanity.com/paper/2501.00430/) (2024)
- [Real-Time Reasoning Agents in Evolving Environments](https://mlsanity.com/paper/2511.04898/) (2025)
- [Dynamic Speculative Agent Planning](https://mlsanity.com/paper/2509.01920/) (2025)
- [UXAgent: A System for Simulating Usability Testing of Web Design with LLM Agents](https://mlsanity.com/paper/2504.09407/) (2025)
- [Strategize Globally, Adapt Locally: A Multi-Turn Red Teaming Agent with Dual-Level Learning](https://mlsanity.com/paper/2504.01278/) (2025)
- [Agentic Knowledgeable Self-awareness](https://mlsanity.com/paper/2504.03553/) (2025)
- [Fast-Slow-Thinking: Complex Task Solving with Large Language Models](https://mlsanity.com/paper/2504.08690/) (2025)
- [DSADF: Thinking Fast and Slow for Decision Making](https://mlsanity.com/paper/2505.08189/) (2025)
- [Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions](https://mlsanity.com/paper/2502.13135/) (2025)
- [UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces](https://mlsanity.com/paper/2505.00472/) (2025)
- [Interact, Instruct to Improve: A LLM-Driven Parallel Actor-Reasoner Framework for Enhancing Autonomous Vehicle Interactions](https://mlsanity.com/paper/2503.00502/) (2025)
- [BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answering](https://mlsanity.com/paper/2505.11811/) (2025)
- [AgentCoMa: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarios](https://mlsanity.com/paper/2508.19988/) (2025)
- [AIvilization v0: Toward Large-Scale Artificial Social Simulation with a Unified Agent Architecture and Adaptive Agent Profiles](https://mlsanity.com/paper/2602.10429/) (2026)
- [Let's Get You Hired: A Job Seeker's Perspective on Multi-Agent Recruitment Systems for Explaining Hiring Decisions](https://mlsanity.com/paper/2505.20312/) (2025)
- [SimuPanel: A Novel Immersive Multi-Agent System to Simulate Interactive Expert Panel Discussion](https://mlsanity.com/paper/2506.16010/) (2025)
- [Thinking While Speaking: Inference-Time Knowledge Transfer for Responsive and Intelligent Conversational Voice Agents](https://mlsanity.com/paper/2511.07397/) (2025)
- [Run, Ruminate, and Regulate: A Dual-process Thinking System for Vision-and-Language Navigation](https://mlsanity.com/paper/2511.14131/) (2025)
- [iSHIFT: Lightweight Slow-Fast GUI Agent with Adaptive Perception](https://mlsanity.com/paper/2512.22009/) (2025)
- [VoiceMem: Streaming Dual-Brain Memory for Real-Time Interaction](https://mlsanity.com/paper/2608.26005/) (2026)
- [MeetBench-XL: Calibrated Multi-Dimensional Evaluation and Learned Dual-Policy Agents for Real-Time Meetings](https://mlsanity.com/paper/2602.03285/) (2026)
- [DuCCAE: A Hybrid Engine for Immersive Conversation via Collaboration, Augmentation, and Evolution](https://mlsanity.com/paper/2603.19248/) (2026)
- [System Switch: When Should a Fast Decision Model Stop and Think?](https://mlsanity.com/paper/2610.09683/) (2026)
- [Polymorphic Combinatorial Frameworks (PCF): Guiding the Design of Mathematically-Grounded, Adaptive AI Agents](https://mlsanity.com/paper/2508.01581/) (2025)
- [Prepared mind, fast response: A temporal decoupling framework for adaptive knowledge orchestration in open-domain dialogue](https://mlsanity.com/paper/2510.08175/) (2025)

## Abstract (arXiv)

Large language models have enabled agents of all kinds to interact with users through natural conversation. Consequently, agents now have two jobs: conversing and planning/reasoning. Their conversational responses must be informed by all available information, and their actions must help to achieve goals. This dichotomy between conversing with the user and doing multi-step reasoning and planning can be seen as analogous to the human systems of "thinking fast and slow" as introduced by Kahneman. Our approach is comprised of a "Talker" agent (System 1) that is fast and intuitive, and tasked with synthesizing the conversational response; and a "Reasoner" agent (System 2) that is slower, more deliberative, and more logical, and is tasked with multi-step reasoning and planning, calling tools, performing actions in the world, and thereby producing the new agent state. We describe the new Talker-Reasoner architecture and discuss its advantages, including modularity and decreased latency. We ground the discussion in the context of a sleep coaching agent, in order to demonstrate real-world relevance.