MapTab: Can MLLMs Master Constrained Route Planning?
arXiv:2602.18600v1 Announce Type: new Abstract: Systematic evaluation of Multimodal Large Language Models (MLLMs) is crucial for advancing Artificial General Intelligence (AGI). However, existing benchmarks remain insufficient for rigorously assessing their constrained reasoning capabilities. To bridge this gap, we introduce MapTab, a multimodal benchmark specifically designed to evaluate constrained reasoning in MLLMs via route planning tasks. MapTab requires MLLMs to perceive and ground visual cues from map images alongside route attributes (e.g., Time, Price) from structured tabular data. The benchmark encompasses two scenarios: Metromap, covering metro networks in 160 cities across 52 countries, and Travelmap, depicting 168 representative tourist attractions from 19 countries. In total, MapTab comprises 328 images, 196,800 route planning queries, and 3,936 QA queries, all incorporating 4 key constraints: Time, Price, Comfort, and Reliability. Extensive evaluations across 15 repres
arXiv:2602.18600v1 Announce Type: new Abstract: Systematic evaluation of Multimodal Large Language Models (MLLMs) is crucial for advancing Artificial General Intelligence (AGI). However, existing benchmarks remain insufficient for rigorously assessing their constrained reasoning capabilities. To bridge this gap, we introduce MapTab, a multimodal benchmark specifically designed to evaluate constrained reasoning in MLLMs via route planning tasks. MapTab requires MLLMs to perceive and ground visual cues from map images alongside route attributes (e.g., Time, Price) from structured tabular data. The benchmark encompasses two scenarios: Metromap, covering metro networks in 160 cities across 52 countries, and Travelmap, depicting 168 representative tourist attractions from 19 countries. In total, MapTab comprises 328 images, 196,800 route planning queries, and 3,936 QA queries, all incorporating 4 key constraints: Time, Price, Comfort, and Reliability. Extensive evaluations across 15 representative MLLMs reveal that current models face substantial challenges in constrained multimodal reasoning. Notably, under conditions of limited visual perception, multimodal collaboration often underperforms compared to unimodal approaches. We believe MapTab provides a challenging and realistic testbed to advance the systematic evaluation of MLLMs.
Executive Summary
This article presents MapTab, a multimodal benchmark designed to evaluate the constrained reasoning capabilities of Multimodal Large Language Models (MLLMs) in route planning tasks. MapTab requires MLLMs to perceive visual cues from map images and route attributes from structured tabular data, incorporating four key constraints: Time, Price, Comfort, and Reliability. The benchmark comprises 328 images, 196,800 route planning queries, and 3,936 QA queries, showcasing the challenges faced by current MLLMs in constrained multimodal reasoning. The study highlights the need for a systematic evaluation of MLLMs, emphasizing the potential of MapTab as a realistic testbed for advancing AGI.
Key Points
- ▸ MapTab is a multimodal benchmark for evaluating constrained reasoning in MLLMs.
- ▸ The benchmark requires MLLMs to perceive visual cues and route attributes from structured data.
- ▸ Current MLLMs face significant challenges in constrained multimodal reasoning.
- ▸ MapTab provides a realistic testbed for advancing AGI.
Merits
Strength in Design
MapTab's design effectively integrates visual and structured data, providing a comprehensive evaluation of MLLMs' constrained reasoning capabilities.
Practical Utility
MapTab's extensive dataset and diverse scenarios make it a valuable resource for researchers and developers seeking to improve MLLMs' performance in route planning tasks.
Demerits
Limited Generalizability
MapTab's focus on route planning tasks may limit its generalizability to other domains, potentially restricting the benchmark's applicability to more comprehensive evaluations of MLLMs.
Scalability Concerns
The large-scale dataset and complex scenarios in MapTab may pose scalability challenges for existing MLLMs, potentially exacerbating the benchmark's evaluation difficulties.
Expert Commentary
The introduction of MapTab represents a significant contribution to the field of MLLMs, as it provides a much-needed benchmark for evaluating constrained reasoning capabilities in these models. While the study's findings are encouraging, they also underscore the substantial challenges that remain in developing MLLMs that can effectively handle complex, real-world scenarios. As the field continues to evolve, it is essential to prioritize the development of more comprehensive evaluation frameworks, such as MapTab, to ensure that MLLMs are equipped to address the needs of a rapidly changing world.
Recommendations
- ✓ Developing more advanced evaluation frameworks that incorporate diverse scenarios and constraints to better assess MLLMs' capabilities.
- ✓ Investigating the potential applications of MapTab in other domains, such as healthcare, finance, and education, to expand its utility and relevance.
