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Agents Thinking Fast and Slow: A Talker-Reasoner Architecture

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arxiv 2410.08328 v1 pith:KHWUL4ZA submitted 2024-10-10 cs.AI cs.CLcs.LG

classification cs.AIcs.CLcs.LG
keywords agentagentsfastplanningreasoningactionsarchitectureconversational
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Let's Get You Hired: A Job Seeker's Perspective on Multi-Agent Recruitment Systems for Explaining Hiring Decisions

    cs.CY 2025-05 conditional novelty 6.0 of 10

    A multi-agent LLM chatbot for job seekers was perceived by 20 interviewed participants as more actionable, trustworthy, and fair than their recalled experiences with traditional hiring methods.

  2. KC-Agent: A Dual-Process Cognitive Architecture for Efficient ML Model Improvement

    cs.AI 2026-08 reject novelty 5.0 of 10

    A dual-process LLM agent (System 1 fast retrieval + System 2 atomic changes) is claimed to improve drifted ML models faster and more accurately, but the reported gains are weakened by evaluating on the same data used ...

  3. AIvilization v0: Toward Large-Scale Artificial Social Simulation with a Unified Agent Architecture and Adaptive Agent Profiles

    cs.MA 2026-02 reject novelty 5.0 of 10

    A deployed LLM-agent society with branch-thinking planning and dual-process memory claims to reproduce heavy-tailed returns, volatility clustering, and education-driven wealth stratification, but key evidence reduces ...

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