Summary of Visual Reasoning and Multi-agent Approach in Multimodal Large Language Models (mllms): Solving Tsp and Mtsp Combinatorial Challenges, by Mohammed Elhenawy et al.
Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challenges
by Mohammed Elhenawy, Ahmad Abutahoun, Taqwa I.Alhadidi, Ahmed Jaber, Huthaifa I. Ashqar, Shadi Jaradat, Ahmed Abdelhay, Sebastien Glaser, Andry Rakotonirainy
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Emerging Technologies (cs.ET); Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA)
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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 Multimodal Large Language Models (MLLMs) are capable of tackling complex problems, including zero-shot in-context learning scenarios. This study explores the ability of MLLMs to visually solve the Traveling Salesman Problem (TSP) and Multiple Traveling Salesman Problem (mTSP) using images that portray point distributions on a two-dimensional plane. The authors introduce a novel approach employing multiple specialized agents within the MLLM framework, each dedicated to optimizing solutions for these combinatorial challenges. The study includes rigorous evaluations across zero-shot settings and introduces innovative multi-agent zero-shot in-context scenarios. The results demonstrated that both multi-agent models significantly improved solution quality for TSP and mTSP problems. The findings showcase the robust visual reasoning capabilities of MLLMs in addressing diverse combinatorial problems, offering insights that could inspire further advancements in this promising field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MLLMs are super powerful tools! They can solve really hard math problems by looking at pictures. In this study, people taught them to solve two special math problems called TSP and mTSP. The MLLMs got better and better at solving these problems as they learned. The results were amazing! |
Keywords
» Artificial intelligence » Zero shot
