arXiv:2602.18600v3 Announce Type: replace 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 reasoning capabilities under multi-criteria constraints. To bridge this gap, we introduce MapTab, a multimodal benchmark specifically designed to evaluate holistic multi-criteria 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, P
The rapid advancement of MLLMs necessitates more sophisticated evaluation benchmarks focusing on complex reasoning, especially as AGI development progresses, mirroring current efforts to improve AI robustness.
A refined benchmark for multi-criteria reasoning in MLLMs is crucial for identifying key limitations and guiding future research toward more capable and reliable AI systems, essential for real-world deployment.
The introduction of MapTab shifts the focus of MLLM evaluation from simple perceptual tasks to complex, multi-modal reasoning capabilities required for practical applications like sophisticated planning.
- · AI researchers
- · Multimodal AI developers
- · Logistics and planning software
- · Autonomous systems
- · Developers of simplistic MLLM benchmarks
- · MLLMs lacking robust reasoning
- · Systems focused only on visual perception
MapTab provides a standardized, challenging benchmark for MLLMs, revealing strengths and weaknesses in multi-criteria reasoning.
Improved MLLMs, guided by such benchmarks, will accelerate the development of more intelligent and versatile AI agents capable of complex tasks.
The enhanced reasoning capabilities could enable new applications in areas like supply chain optimization, smart city management, and advanced robotic navigation.
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Read at arXiv cs.LG