According to God of Prompt on Twitter, recent CIFAR-10 benchmark results demonstrate a significant leap in balanced accuracy (0.781 vs 0.711 baseline) and recall (0.821 vs 0.480 baseline), all achieved with ten times greater parameter efficiency. The model's ability to allocate capacity where it matters most suggests promising advancements in efficient neural network design, offering new business opportunities for companies seeking to deploy high-performing machine learning models on limited hardware. As highlighted by God of Prompt, these results can drive innovation in edge computing and real-time AI applications.

According to @godofprompt, Alibaba has released Tongyi DeepResearch, a 30B parameter open-source AI agent that outperforms GPT-4o and DeepSeek-V3 in deep research tasks while using just 3.3B active parameters (source: https://twitter.com/godofprompt/status/1983836518067401208). Unlike the industry trend of scaling to 600B+ parameters, Alibaba's innovation lies in its training approach. The model introduces 'agentic mid-training,' an intermediate phase that teaches the AI how to act as an agent before learning specific tasks, bridging the gap between language pre-training and task-specific post-training. This paradigm shift addresses the alignment issues seen in traditional supervised fine-tuning and reinforcement learning. All training data is AI-generated, with no human annotation, and includes complex, multi-hop reasoning samples. The model achieves state-of-the-art results: 32.9% on Humanity's Last Exam, 43.4% on BrowseComp, and 75% on xbench-DeepSearch. Remarkably, training was done on just two H100 GPUs for two days at under $500 per task. This demonstrates significant business opportunities for cost-efficient, high-performing AI agents and signals a shift toward smarter training over brute-force scaling (source: arxiv.org/abs/2510.24701; github.com/Alibaba-NLP/DeepResearch).