Core Insights - Tongyi's first deep research agent model, DeepResearch, has been officially open-sourced, featuring a parameter size of only 30 billion (with 3 billion activated), achieving state-of-the-art (SOTA) results across multiple authoritative evaluation sets, surpassing many top agent models [1][5] Model Training - The Tongyi team has developed a complete training pipeline driven by synthetic data, integrating pre-training and post-training phases. This model capability is based on a multi-stage data strategy aimed at creating vast amounts of high-quality training data without relying on expensive manual annotations [3] - The training pipeline is optimized based on the Qwen3-30B-A3B model, incorporating innovative reinforcement learning (RL) algorithms for validation and real training, enhancing model efficiency and robustness. The use of asynchronous reinforcement learning algorithms and automated data curation processes significantly boosts the model's iteration speed and generalization ability [3] Model Performance - The DeepResearch model, with 3 billion activated parameters, performs comparably to flagship models such as OpenAI's o3, DeepSeek V3.1, and Claude-4-Sonnet in various authoritative agent evaluation sets, including Humanity's Last Exam (HLE), BrowseComp, and GAIA [5] Model Applications - The model has been applied in various real-world scenarios, such as the "Xiao Gao Teacher" developed in collaboration with Amap, which acts as an AI co-pilot for complex travel planning tasks. Additionally, Tongyi's legal research agent, empowered by the DeepResearch architecture, can autonomously execute complex multi-step research tasks, simulating the workflow of a junior lawyer [7] DeepResearch Agent Series - Tongyi DeepResearch also boasts a rich family of DeepResearch Agent models. Earlier this year, the team has continuously expanded its DeepResearch offerings, with previously open-sourced models like WebWalker, WebDancer, and WebSailor achieving industry-leading results in agent synthetic data and reinforcement learning [9]

Core Insights - The first deep research Agent model, DeepResearch, developed by Tongyi, has been officially open-sourced [1] - The model has 30 billion parameters (with 3 billion activated) and has achieved state-of-the-art (SOTA) results on multiple authoritative evaluation datasets [1] Company Summary - Tongyi has introduced its first deep research model, indicating a significant advancement in AI technology [1] - The open-sourcing of DeepResearch may enhance collaboration and innovation within the AI research community [1] Industry Summary - The achievement of SOTA results by DeepResearch highlights the competitive landscape in AI model development [1] - The model's parameters and performance metrics suggest a trend towards larger and more efficient AI models in the industry [1]

Core Insights - Tencent Hunyuan has launched four small-sized models: 0.5B, 1.8B, 4B, and 7B, all of which are open-source and can run on consumer-grade graphics cards [1] - The models are compatible with major chip platforms including ARM, Qualcomm, MTK, and Intel, making them suitable for various devices such as smartphones, computers, smart cars, and smart home devices [1] - The new models have demonstrated leading performance in language understanding, mathematics, and reasoning during testing [1] Model Specifications - Hunyuan-4B: - Low power consumption and high efficiency - Maximum input and output of 32K - Suitable for real-time response scenarios with strong inference performance and accuracy - Supports various verticals like finance, education, and healthcare with cost-effective fine-tuning [2] - Hunyuan-7B: - High cost-performance ratio - Can run efficiently on consumer-grade GPUs - Knowledge density significantly higher than other models of similar size, approaching the performance of larger models - Offers flexibility in balancing response rate and depth, with a 256K ultra-long context window [2]