Science communication revision requires writers to dynamically balance scientific exposition and narrative engagement - a process where writers often struggle with competing directions. Existing LLM-assisted tools help with co-writing, but offer limited support for navigating this iterative, multi-directional revision process. To address this gap, we designed Spatial Balancing, an exploratory revision environment that maps rhetorical goals and revision strategies onto a two-dimensional spatial canvas for experienced science communication creators with domain expertise but lacking formal professional training. By building a design space of communication strategies and embedding them into a spatial exploratory canvas, our system treats feedback as navigational cues rather than prescriptive judgments. Our findings show that this integrated revision environment helps writers stay focused on writing goals, reason about revision as trajectories, and explore alternatives, which supports greater metacognitive control and confidence without increasing workload. This work highlights the value of spatially externalized revision environments for supporting iterative, reflective thinking during LLM-assisted writing.

Humans think visually—we remember in images, dream in pictures, and use visual metaphors to communicate. Yet, most creative writing tools remain text-centric, limiting how writers plan and translate ideas. We present Vistoria, a system for synchronized image-text co-editing in fictional story writing. A formative Wizard-of-Oz co-design study with 10 story writers revealed how sketches, images, and text serve as essential elements for ideation and organization. Drawing on theories of Instrumental Interaction, Vistoria introduces instrumental operations—Lasso, Collage, Perspective Shift, and Filter that enable seamless narrative exploration across modalities. A controlled study with 12 participants shows that co-editing enhances expressiveness, immersion, and collaboration, opening space for writers to follow divergent story directions and craft more vivid, detailed narratives. While multimodality increased cognitive demand, participants reported stronger senses of ownership and agency. These findings demonstrate how multimodal co-editing expands creative potential by balancing abstraction and concreteness in narrative development.

Digital Compute-in-Memory (DCIM), which inserts logic circuits into SRAM arrays, presents a significant advancement in CIM architecture. DCIM has shown great potential in applications, and the diversity of applications requires rapid hardware iteration. However, the hardware design flow from user specifications to layout is extremely tedious and time-consuming for manual design. Commercial EDA tools are limited by restrictive licenses and the inability to specifically optimize the datapath, which calls for an open-source end-to-end hardware compiler for DCIM. This paper proposes$\mathbf{O}\mathbf{p}\mathbf{e}\mathbf{n}\mathbf{C}^{2}$, the first open-source end-to-end development framework for DCIM macro compilation.$\mathbf{O}\mathbf{p}\mathbf{e}\mathbf{n}\mathbf{C}^{2}$provides a template-based generation platform for DCIM macros across various technologies, sizes, and configurations. It can automatically generate a datapath-optimized, compact DCIM macro layout based on a hierarchical physical design methodology. Our experiment results show that$\mathbf{O}\mathbf{p}\mathbf{e}\mathbf{n}\mathbf{C}^{2}$'s compact design on FreePDK45 delivers over 30% area reduction and over 40% improvement in area efficiency compared to AutoDCIM on TSMC40.

Transformer models have shown remarkable performance in AI tasks. However, their large model sizes and large-scale matrix computations heavily press the memory bandwidth. Compute-in-memory (CIM) solutions have emerged to address these issues by integrating computation into memory. Nevertheless, existing scalable and heterogeneous CIM designs further suffer from inefficiencies caused by isolated mapping strategies and communication redundancy. To address these issues, we propose Cross-Type Mapped Dataflow, an architecture-dataflow co-design that enables efficient and collaborative analog-digital CIM execution.Our approach features a heterogeneous analog-digital CIM architecture with flat network-on-chip (NoC) interconnects, enabling seamless cross-type communication. The core innovations include: a Hybrid Dataflow that schedules idle CIMs for cross-type collaboration on vector-matrix multiplication, and an In-Network Reduction (INR) mechanism that eliminates redundant data transfers by embedding Reduction operations within NoC communication. Experimental results show an average 3.57× speedup and 2.78× energy efficiency improvement over Homogeneous DCIM architecture. Optimized by the proposed Hybrid Dataflow and INR, our heterogeneous CIMs further achieve an average 4.63× speedup and 2.59× energy efficiency improvement over state-of-the-art X-Former-like design across various ViT workloads.

Design rule checking (DRC) is an essential procedure in physical verification, yet few open-source DRC tools are accessible in academia. To fill in the gap, we present OpenDRC, an open-source DRC engine that aims for extremely high efficiency. OpenDRC maintains hierarchical layouts with layer-wise bounding volume hierarchies and performs adaptive row-based partition to identify independent regions for check pruning and/or parallel processing. For common design rules, OpenDRC provides a sequential mode that runs cell-level sweeplines, and a parallel mode that launches edge-based GPU check kernels. Experiments demonstrate that OpenDRC outperforms state-of-the-art multi-threading and GPU-accelerated design rule checkers. The source code is available at https://github.com/opendrc/opendrc.