arXiv:2605.23883v1 Announce Type: cross Abstract: Despite remarkable progress in Multimodal Large Language Models (MLLMs), these models still struggle with fine-grained understanding tasks. In this work, we propose Procedurally Generated Tasks (PGT), a simple data-driven framework that serves a dual purpose: inducing fine-grained visual understanding and acting as a low-cost diagnostic tool to identify the source of perception failures. By overlaying unambiguous geometric primitives on images, PGT generate additional dense supervision that disentangles visual grounding capability from semantic
The rapid development of MLLMs necessitates more robust evaluation and training methods to address persistent challenges in fine-grained understanding.
Improving MLLM capabilities in fine-grained visual understanding is critical for their deployment in complex tasks, especially those requiring precise spatial and object recognition.
A new, low-cost method for generating dense supervision data and diagnosing perception failures in MLLMs is introduced, potentially accelerating MLLM development and reliability.
- · AI researchers
- · MLLM developers
- · Computer vision companies
- · Companies relying on expensive, manual data annotation for MLLMs
The PGT framework enables more efficient training and debugging of MLLMs, leading to improved model performance.
Enhanced MLLM capabilities could accelerate the development of more reliable AI agents and advanced automation systems.
Deeper visual grounding might allow AI systems to tackle more nuanced real-world problems, impacting various industries by automating tasks currently requiring human visual interpretation.
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Read at arXiv cs.AI