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cs.CV2026

SUPERGLASSES: Benchmarking Vision Language Models as Intelligent Agents for AI Smart Glasses

Zhuohang Jiang, Xu Yuan, Haohao Qu +4

The rapid advancement of AI-powered smart glasses-one of the hottest wearable devices-has unlocked new frontiers for multimodal interaction, with Visual Question Answering (VQA) ov…

cs.CV2026

AdaptMMBench: Benchmarking Adaptive Multimodal Reasoning for Mode Selection and Reasoning Process

Xintong Zhang, Xiaowen Zhang, Jingrong Wu +8

Adaptive multimodal reasoning has emerged as a promising frontier in Vision-Language Models (VLMs), aiming to dynamically modulate between tool-augmented visual reasoning and text…

cs.CV2026

V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval

Dongyang Chen, Chaoyang Wang, Dezhao Su +6

Multimodal Large Language Models (MLLMs) have recently been applied to universal multimodal retrieval, where Chain-of-Thought (CoT) reasoning improves candidate reranking. However,…

cs.CV2026

STVG-R1: Incentivizing Instance-Level Reasoning and Grounding in Videos via Reinforcement Learning

Xiaowen Zhang, Zhi Gao, Licheng Jiao +2

In vision-language models (VLMs), misalignment between textual descriptions and visual coordinates often induces hallucinations. This issue becomes particularly severe in dense pre…

cs.CV2026

Towards Artwork Explanation in Large-scale Vision Language Models

Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2

Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…