SADU benchmark shows top VLMs reach only 70% accuracy on software architecture diagram tasks, revealing gaps in visual reasoning for engineering artifacts.
Do vision- language models really understand visual language? arXiv preprint arXiv:2410.00193
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Staged post-training that first solidifies visual perception before visual and textual reasoning improves VLM accuracy and shortens reasoning traces on visual math and perception benchmarks.
VLMs show systematic drops in counting accuracy as visual and linguistic complexity rise, with modest gains from targeted attention reweighting in the decoder.
citing papers explorer
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Benchmarking and Evaluating VLMs for Software Architecture Diagram Understanding
SADU benchmark shows top VLMs reach only 70% accuracy on software architecture diagram tasks, revealing gaps in visual reasoning for engineering artifacts.
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From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models
Staged post-training that first solidifies visual perception before visual and textual reasoning improves VLM accuracy and shortens reasoning traces on visual math and perception benchmarks.
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Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions
VLMs show systematic drops in counting accuracy as visual and linguistic complexity rise, with modest gains from targeted attention reweighting in the decoder.