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该论文提出Image Factuality Assessment任务及EviScene多模态ReAct智能体，通过视觉检查、外部检索与证据分析的交替推理来判定图像所描绘场景是否事实成立，并发布了包含10,174张图像的EviLens专家审阅基准，在Qwen3.5-9B上实现了79.05%的平衡准确率。
This paper proposes VaseMuseum, a lightweight multimodal agent framework that combines 3D digitization with vision-language models to create an interactive digital museum for ancient Greek pottery, addressing challenges of evidence grounding and hallucination through source-level and response-level reliability control.
This paper introduces CanvasCraft, a large-scale multimodal tool-use dataset for complex image creation and editing, and CanvasAgent, a tool-augmented multimodal agent that learns to orchestrate heterogeneous visual tools through multi-turn interactions and hybrid reward optimization.
Light-Omni is a multimodal agent framework for efficient video understanding that uses dual contextual states (global state and parametric latent state) to avoid iterative reasoning, achieving faster and more accurate processing with significant speedup and memory savings.
Visual-Seeker proposes a visual-native multimodal deep search agent that actively reasons over fine-grained visual details and synthesizes multimodal evidence, achieving state-of-the-art performance on five challenging multimodal search benchmarks.
VisualClaw is a self-evolving multimodal agent that reduces deployment costs through hybrid encoding and skill evolution, while improving video-QA accuracy across multiple benchmarks.
ByteDance Seed has open-sourced the TaskMem checkpoint, trained on Qwen3-VL-30B-A3B. It uses two-stage reinforcement learning to enable multimodal Agents to learn to generate long-term memory from video streams, achieving significant improvements on benchmarks such as VideoMME and EgoLife.