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cs.CV2026
ConvCue: Complementary Visual Inductive Biases for Vision-Language Models
Zixuan Lan, Shichu Sun
Modern vision-language models (VLMs) achieve strong performance across a broad range of multimodal tasks, yet still struggle with visual questions that require fine-grained discrim…
cs.CV2026
SABRE: Scalable and Automated Benchmarking of VLMs under Stress
Zixuan Lan, Luzhe Sun, Matthew R. Walter +1
Vision-language models (VLMs) are improving rapidly, but benchmark development lags behind, making weaknesses hard to identify. Building stress tests is costly: samples must satisf…
cs.CV2026
Seeing without Looking: Do Vision-Language Benchmarks Really Test Vision?
Zixuan Lan, Luzhe Sun, Matthew R. Walter +1
Benchmark accuracy is often implicitly assumed to reflect grounded visual understanding in vision-language models (VLMs), yet it remains unclear to what extent such scores truly re…