Current genomic foundation models (GFMs) rely largely on extensive neural computation to implicitly infer conserved biological motifs from single-nucleotide inputs. We introduce Gengram, a conditional memory module that provides an explicit and efficient lookup primitive for multi-base motifs through a genome-specific hashing scheme. Integrated into state-of-the-art (SOTA) GFM backbones, Gengram achieves substantial improvements of up to 22.6% across functional genomics tasks and generalizes across diverse architectures. Further analysis of Gengram’s latent space reveals biologically meaningful representations aligned with established genomic principles. By introducing structured motif memory as a modeling primitive, Gengram improves empirical performance and mechanistic interpretability, offering a scalable and biology-aligned direction for next-generation GFMs. The code is available at https://github.com/zhejianglab/Gengram, and the model weights are available at https://huggingface.co/ZhejiangLab/Gengram.

This paper introduces Light-R1, an opensource suite for training long reasoning modelsusing reproducible and cost-effective methodology. Given the proprietary nature of data usedin the DeepSeek-R1 series, we develop an alternative approach leveraging exclusively publicdata and models. Our curriculum training progressively increases data difficulty, combinedwith multi-staged post-training. Our LightR1-32B model, trained from Qwen2.5-32BInstruct, outperforms DeepSeek-R1-DistillQwen-32B in math reasoning. Experimental results show that this curriculum approachbecomes more effective when distinct, diverse datasets are available for different training stages: fine-tuning DeepSeek-R1-Distilledmodels (pre-tuned by DeepSeek team on proprietary data) with 3,000 challenging examplesfrom our curriculum dataset yielded state-ofthe-art 7B and 14B models, while the 32Bmodel, Light-R1-32B-DS performed comparably to QwQ-32B and DeepSeek-R1. Furthermore, we extend our work by applying GRPOon long reasoning models. Our final Light-R1-14B-DS achieves SOTA performance among14B models in math, with AIME24 & 25 scoresof 74.0 and 60.2 respectively, surpassing many32B models and DeepSeek-R1-Distill-Llama70B. Despite math-focused training, Light-R1-14B-DS demonstrates strong cross-domain generalization. Light-R1 represents a significantadvancement in making sophisticated reasoning models more accessible and implementablein real-world applications. Our models, training data and code have been made available.