The day's essential AI stories, from the labs, startups and researchers shaping it.
The DecoderFeatured7272Why it matters: The original text provides a comparison of the promotional rate and fact accuracy rate before and after fine-tuning, allowing readers to judge whether the political tendency of Chinese open-source models only stays on the surface.
Hugging Face BlogFeatured6060IBM's research team fine-tunes a 20B open-source weight model, Palette, using a two-role LoRA scheme where designers write JSON briefs and encoders generate pptxgenjs code page by page to create presentations according to IBM Design Language.
Why it matters: IBM uses synthetic data and checks to repair closed-loop training for a 20B small model to create presentations, providing a method that can be transferred to other structured outputs.
Hugging Face BlogFeatured6262The IBM team compared CUGA, Hermes, and Deep Agents using the same open-source weight model GLM-5.3-flash on 90 tasks in AppWorld, with pass rates of 81.1%, 46.7%, and 30.0%, respectively, and 20, 8, and 2 scenarios where all three variants passed.
Why it matters: With the same model but a different harness, the task completion rate increased from 30% to 81%, and reproducible configurations and evaluation links were provided in the text.
Hugging Face BlogFeatured6666Microsoft and Hugging Face release ThinkingBox-Training, directly using ThinkingBox's deterministic verification of database final states as reinforcement learning rewards, training Qwen3.8-27B for 50 steps with GRPO
Why it matters: Microsoft and Hugging Face directly use database final state verification as reinforcement learning rewards and have publicly released task filtering and training code, which can be transferred to other stateful intelligent agent training
