A well-known pitfall of molecular generative models is that they are not guaranteed to generate synthesizable molecules. Existing solutions for this problem often struggle to effectively navigate exponentially large combinatorial space of synthesizable molecules and suffer from poor coverage. To address this problem, we introduce ReaSyn, an iterative generative pathway refinement framework that obtains synthesizable analogs to input molecules by projecting them onto synthesizable space.

Protein interaction modeling is central to protein design, which has been transformed by machine learning with applications in drug discovery and beyond. In this landscape, structure-based de novo binder design is cast as either conditional generative modeling or sequence optimization via structure predictors ("hallucination"). We argue that this is a false dichotomy and propose Proteina-Complexa, a novel fully atomistic binder generation method unifying both paradigms.

Recently, many generative models for de novo protein structure design have emerged. Yet, only few tackle the difficult task of directly generating fully atomistic structures jointly with the underlying amino acid sequence. This is challenging, for instance, because the model must reason over side chains that change in length during generation.

GPUs play an increasingly important role in modern software. However, the heterogeneous host-device execution model and expanding software stack make GPU programs prone to memory-safety and concurrency bugs that evade static analyses. While fuzz-testing, combined with dynamic error checking tools, offers a plausible solution, it remains underutilized for GPUs.

ALPhA-Vision is an always-on low-power subsystem for DNN-inference-based vision tasks in edge SoCs. Flexible and programmable, the subsystem supports CNN and ViT inference and employs hardware/software co-design to enable fully end-to-end execution with no external memory accesses. Fine-grained power management features to mitigate leakage enable the subsystem to perform face detection with 787µs latency and 99.3% detection accuracy with 4.6 mW average power at 60fps.

Diffusion models have revolutionized video generation, becoming essential tools in creative content generation and physical simulation. Transformer-based architectures (DiTs) and classifier-free guidance (CFG) are two cornerstones of this success, enabling strong prompt adherence and realistic video quality. Despite their versatility and superior performance, these models require intensive computation. Each video generation requires dozens of iterative steps, and CFG doubles the required compute. This inefficiency hinders broader adoption in downstream applications.