Training LLMs for tool use requires more than final-answer supervision: models need trajectories that specify how to decompose a question, call tools across turns, use retrieved evidence, and recover from earlier errors. Existing data construction pipelines often rely on large-scale real-world tool API calls or simple templates, making them costly or limited in interaction coverage. We introduce ToolForge, a virtual-tool data synthesis pipeline for constructing tool-use datasets with three targeted abilities: multi-hop reasoning, multi-turn tool use, and self-reflection. Starting from QA seed data, ToolForge builds an extensible tool space from 19 base virtual tools and automatically generated variants, selects among four tool-use paradigms, simulates both successful and misleading retrieval outcomes, and generates validated tool-use dialogues without large-scale real-world tool API execution. A Multi-Layer Validation module filters invalid traces and is audited with hard negatives mined by Monte Carlo Tree Search. Fine-tuning Qwen3-8B on only 4,250 ToolForge samples produces a model named ToolForge-8B, which improves over the Tool-Integrated Reasoning baseline by 21.6% on five out-of-domain QA benchmarks and remains competitive on BFCL and API-Bank. We further quantify the pipeline construction cost, including tool initialization, dialogue generation, validation, and SFT, and compare ToolForge with existing synthetic tool-data sources. Our code and dataset are publicly available at https://github.com/Buycar-arb/ToolForge.

Recent progress in Multimodal Large Language Models (MLLMs) has significantly enhanced the ability of artificial intelligence systems to understand and generate multimodal content. However, the capabilities of existing MLLMs in understanding traditional Chinese culture have not yet been comprehensively explored. To address this gap, we propose the Traditional Chinese Culture understanding Benchmark (TCC-Bench), a bilingual Visual Question Answering (VQA) benchmark designed to assess MLLMs’ performance on culturally grounded visual content. TCC-Bench features culturally rich and visually diverse data, covering museum artifacts, daily life scenes, comics, and other culturally significant contexts across eight cultural domains. Through extensive experiments on a wide range of MLLMs, we find that current models still face substantial challenges in handling traditional Chinese cultural content. Our results highlight the need for more culturally inclusive and context-aware multimodal systems. The code and data can be found at: https://github.com/Morty-Xu/TCC-Bench.