Summary of Fleet Of Agents: Coordinated Problem Solving with Large Language Models Using Genetic Particle Filtering, by Akhil Arora et al.
Fleet of Agents: Coordinated Problem Solving with Large Language Models using Genetic Particle Filtering
by Akhil Arora, Lars Klein, Nearchos Potamitis, Roland Aydin, Caglar Gulcehre, Robert West
First submitted to arxiv on: 7 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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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 paper introduces Fleet of Agents (FoA), a novel framework that utilizes large language models (LLMs) as agents to navigate through dynamic tree searches. FoA spawns multiple autonomous agents, each exploring its own path, followed by a selection phase that optimizes the balance between exploration and exploitation using a heuristic value function. This mechanism enables dynamic branching, adapting the exploration strategy based on discovered solutions. The authors experimentally validate FoA using two benchmark tasks, “Game of 24” and “Mini-Crosswords”, outperforming the previously proposed Tree-of-Thoughts method in terms of efficacy and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FoA is a new way to use big language models like agents to find solutions by searching through many possibilities. It works by creating lots of little agents, each trying different paths, and then choosing the best ones based on how well they’re doing. This helps it find answers more quickly while still being very accurate. |
