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.

LLM-based multi-agent systems (MAS) have shown promise in tackling complex tasks. However, existing solutions often suffer from limited agent coordination and heavy reliance on predefined Standard Operating Procedures (SOPs), which demand extensive human input. To address these limitations, we propose MegaAgent, a large-scale autonomous LLM-based multi-agent system. MegaAgent generates agents based on task complexity and enables dynamic task decomposition, parallel execution, efficient communication, and comprehensive system monitoring of agents. In evaluations, MegaAgent demonstrates exceptional performance, successfully developing a Gobang game within 800 seconds and scaling up to 590 agents in a national policy simulation to generate multi-domain policies. It significantly outperforms existing systems, such as MetaGPT, in both task completion efficiency and scalability. By eliminating the need for predefined SOPs, MegaAgent demonstrates exceptional scalability and autonomy, setting a foundation for advancing true autonomy in MAS.

To fully evaluate the overall performance of different NLP models in a given domain, many evaluation benchmarks are proposed, such as GLUE, SuperGLUE and CLUE. The field of natural language understanding has traditionally focused on benchmarks for various tasks in languages such as Chinese, English, and multilingual, however, there has been a lack of attention given to the area of classical Chinese, also known as “wen yan wen (文言文)”, which has a rich history spanning thousands of years and holds significant cultural and academic value. For the prosperity of the NLP community, in this paper, we introduce the WYWEB evaluation benchmark, which consists of nine NLP tasks in classical Chinese, implementing sentence classification, sequence labeling, reading comprehension, and machine translation. We evaluate the existing pre-trained language models, which are all struggling with this benchmark. We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on classical Chinese NLU. The github repository is https://github.com/baudzhou/WYWEB.