Improving vision-language models (VLMs) on visual reasoning typically requires retraining or hand-designed prompts and tools. We present Dynamo, a training-free framework that adapts a frozen VLM without any weight updates. On a small labeled training subset, the agent inspects its own correct and incorrect attempts and evolves two complementary capabilities: reusable reasoning skills for cognitive bottlenecks, and executable visual tools for perceptual ones. Each generated tool is paired with a skill that specifies when to invoke it, and both capability types accumulate in a persistent library. Across four visual reasoning benchmarks and five VLM backbones, Dynamo improves direct inference on all 20 model–benchmark settings (avg. +5.6 acc). When the tool set is given in advance, the framework learns when to call each tool, and per-step tool choice improves on every tested backbone. Against task-specific RL (VTool-R1, DeepEyes), Dynamo closes 65–99% of the RL gap at a fraction of the compute, and combines additively with RL when available.

“Knowledge graphs are used to alleviate the problems of data sparsity and cold starts in recom-mendation systems. However, most existing approaches ignore the hierarchical structure of theknowledge graph. In this paper, we propose a box embedding method for knowledge graph-enhanced recommendation system. Specifically, the box embedding represents not only the in-teraction between the user and the item, but also the head entity, the tail entity and the relationbetween them in the knowledge graph. Then the interaction between the item and the corre-sponding entity is calculated by the multi-task attention unit. Experimental results show thatour method provides a large improvement over previous models in terms of Area Under Curve(AUC) and accuracy in publicly available recommendation datasets with three different domains.”