Minsu Ha

Junghwan Lim, Wai Ting Cheung, Minsu Ha et al.

Training strong video generation models usually requires massive datasets, large parameter counts, and substantial compute. In this work, we ask whether strong text-to-video quality is possible at a much smaller budget: fewer than 10M clips and less than 100,000 H200 GPU hours. Our core claim is that part of the answer lies in how model capacity is organized, not only in how much of it is used. In video generation, prompt alignment, temporal consistency, and fine-detail recovery can interfere with one another when they are handled through the same pathway. Motif-Video 2B addresses this by separating these roles architecturally, rather than relying on scale alone. The model combines two key ideas. First, Shared Cross-Attention strengthens text control when video token sequences become long. Second, a three-part backbone separates early fusion, joint representation learning, and detail refinement. To make this design effective under a limited compute budget, we pair it with an efficient training recipe based on dynamic token routing and early-phase feature alignment to a frozen pretrained video encoder. Our analysis shows that later blocks develop clearer cross-frame attention structure than standard single-stream baselines. On VBench, Motif-Video~2B reaches 83.76\%, surpassing Wan2.1 14B while using 7$\times$ fewer parameters and substantially less training data. These results suggest that careful architectural specialization, combined with an efficiency-oriented training recipe, can narrow or exceed the quality gap typically associated with much larger video models.

Kayhan Moharreri, Minsu Ha, Ross H Nehm

EvoGrader is a free, online, on-demand formative assessment service designed for use in undergraduate biology classrooms. EvoGrader's web portal is powered by Amazon's Elastic Cloud and run with LightSIDE Lab's open-source machine-learning tools. The EvoGrader web portal allows biology instructors to upload a response file (.csv) containing unlimited numbers of evolutionary explanations written in response to 86 different ACORNS (Assessing COntextual Reasoning about Natural Selection) instrument items. The system automatically analyzes the responses and provides detailed information about the scientific and naive concepts contained within each student's response, as well as overall student (and sample) reasoning model types. Graphs and visual models provided by EvoGrader summarize class-level responses; downloadable files of raw scores (in .csv format) are also provided for more detailed analyses. Although the computational machinery that EvoGrader employs is complex, using the system is easy. Users only need to know how to use spreadsheets to organize student responses, upload files to the web, and use a web browser. A series of experiments using new samples of 2,200 written evolutionary explanations demonstrate that EvoGrader scores are comparable to those of trained human raters, although EvoGrader scoring takes 99% less time and is free. EvoGrader will be of interest to biology instructors teaching large classes who seek to emphasize scientific practices such as generating scientific explanations, and to teach crosscutting ideas such as evolution and natural selection. The software architecture of EvoGrader is described as it may serve as a template for developing machine-learning portals for other core concepts within biology and across other disciplines.

Junghwan Lim, Sungmin Lee, Dongseok Kim et al.

We introduce Motif-2-12.7B-Reasoning, a 12.7B parameter language model designed to bridge the gap between open-weight systems and proprietary frontier models in complex reasoning and long-context understanding. Addressing the common challenges of model collapse and training instability in reasoning adaptation, we propose a comprehensive, reproducible training recipe spanning system, data, and algorithmic optimizations. Our approach combines memory-efficient infrastructure for 64K-token contexts using hybrid parallelism and kernel-level optimizations with a two-stage Supervised Fine-Tuning (SFT) curriculum that mitigates distribution mismatch through verified, aligned synthetic data. Furthermore, we detail a robust Reinforcement Learning Fine-Tuning (RLFT) pipeline that stabilizes training via difficulty-aware data filtering and mixed-policy trajectory reuse. Empirical results demonstrate that Motif-2-12.7B-Reasoning achieves performance comparable to models with significantly larger parameter counts across mathematics, coding, and agentic benchmarks, offering the community a competitive open model and a practical blueprint for scaling reasoning capabilities under realistic compute constraints.