MetaForge: A Multimodal Model that Retrieves, Adapts, and Forges Tools On Demand

Multimodal models achieve strong performance on complex reasoning tasks through tool use, but they still face two limitations. Static, predefined tool inventories fail to meet the needs of unseen tasks, while indiscriminate tool invocation increases cost and introduces noise-induced errors. To address these issues, we propose MetaForge, a multimodal model that learns when to invoke existing tools and when to request new tools for capability gaps. MetaForge organizes tool use into four stages: Decide determines whether tools are needed, Retrieve selects suitable tools, and Adapt adjusts call parameters. Forge generates, validates, and registers new tools when existing tools are insufficient. For registered tools, MetaForge performs tool-level recursive self-improvement during training using execution feedback to improve their cross-task generalization. Meanwhile, we train the orchestration policy with multi-turn GRPO and composite rewards to optimize tool selection, invocation efficiency, tool reuse, and output formatting. Additionally, we propose capacity-constrained dynamic tool pool management to retain better effective tools. Evaluations on 12 benchmarks against 16 baselines show that MetaForge achieves higher overall accuracy across different task and tool settings. These experiments demonstrate the effectiveness of shifting from static tool inventories to on-demand tool evolution.