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20242026
most citedVisual Perception by Large Language Model's Weights

1 citations · 2 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

Neos: Learning to Interleave Search and Visual Reasoning

Ekko He, Jack Hong, Shilin Yan +8

Recent multimodal deep search agents have advanced tool use and visual content retrieval, yet they still struggle to convert intermediate visual evidence into actionable guidance f…

cs.CV2026

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos

Yuqi Tang, Yang Shi, Zhuoran Zhang +21

Recent video generative models have greatly improved the realism of AI-generated videos, yet their outputs still exhibit artifacts such as temporal inconsistencies, structural dist…

cs.CV2026

Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models

Shilin Yan, Jintao Tong, Hongwei Xue +6

The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a profound meta-cognitive deficit…

cs.CV2026

MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning

Haozhan Shen, Shilin Yan, Hongwei Xue +5

Multimodal Large Language Models (MLLMs) are increasingly used to carry out visual workflows such as navigating GUIs, where the next step depends on verified visual compositional c…

cs.CV2026

Beyond Scattered Acceptance: Fast and Coherent Inference for DLMs via Longest Stable Prefixes

Pengxiang Li, Joey Tsai, Hongwei Xue +2

Diffusion Language Models (DLMs) promise highly parallel text generation, yet their practical inference speed is often bottlenecked by suboptimal decoding schedulers. Standard appr…

cs.CV2026

SwimBird: Eliciting Switchable Reasoning Mode in Hybrid Autoregressive MLLMs

Jintao Tong, Shilin Yan, Hongwei Xue +5

Multimodal Large Language Models (MLLMs) have made remarkable progress in multimodal perception and reasoning by bridging vision and language. However, most existing MLLMs perform…