Summary of Agents Thinking Fast and Slow: a Talker-reasoner Architecture, by Konstantina Christakopoulou et al.
Agents Thinking Fast and Slow: A Talker-Reasoner Architecture
by Konstantina Christakopoulou, Shibl Mourad, Maja Matarić
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper explores the dual nature of large language models, which enable agents to interact with users through natural conversation. These agents must balance conversational responses informed by available information with multi-step reasoning and planning to achieve goals. The authors propose a novel Talker-Reasoner architecture, comprising System 1 (Talker) for fast and intuitive conversations and System 2 (Reasoner) for slower, more logical reasoning and planning. This modular approach decreases latency while maintaining real-world relevance, demonstrated through a sleep coaching agent example. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers talk to people like humans do. The authors want to improve how these computer programs communicate by giving them two abilities: talking naturally with users and making smart decisions. They designed a special way for these computer programs to work together, using one part that thinks quickly and another that takes more time to think deeply. This new system helps the computers make better decisions and respond faster while still being useful in real-life situations. |
