As integrated circuits continue to grow in both scale and complexity, macro placement plays a critical role in physical design, directly affecting chip-level performance, power, and area (PPA). Traditional macro placement methods, such as simulated annealing, analytical optimization, and reinforcement learning, face limitations including slow convergence, heavy dependence on large datasets, and over-reliance on intermediate PPA indicators rather than final PPA. Large language models (LLMs) offer strong generative power and semantic reasoning that can potentially automate macro layout tasks while addressing the aforementioned problems in traditional methods, but their limited understanding of layout rules and lack of iterative, feedback-driven refinement make direct application challenging. To address this, we propose CHIP-MAP, a macro placement framework based on multi-agent collaboration and feedback-driven optimization. Furthermore, we introduce two innovative tools: the Module Link Weight Analyzer (MWA) and the Standard Cell Usability Score (SCUS), which are designed to guide fine-grained layout refinement. We evaluate CHIP-MAP on five benchmarks ranging from low-power cores to large multi-core processors implemented at 130nm and 45nm technology nodes. Results show that it achieves up to 1.5% area reduction and an average repair of 61.6% of total negative slack (TNS), while also reducing wirelength and improving timing.

Dummy fill insertion is essential for CMP uniformity but remains challenging due to the nonlinear CMP process, the large optimization space, and timing degradation caused by parasitic coupling. We propose SONIC, a differentiable CMP-driven dummy fill optimization framework that employs a neural CMP simulator to directly optimize planarization objectives using gradient-based methods. SONIC further integrates a timing-aware fill insertion strategy to mitigate coupling capacitance near critical nets. Experimental results demonstrate that SONIC achieves competitive planarization quality with up to 1830× runtime speedup over a full-chip CMP simulator. Compared with the state-of-the-art model-based method, SONIC reduces height variation, line deviation, and outliers by up to 86.16%, 90.10%, and 51.61%, respectively, while achieving a 77.67% runtime reduction and lowering coupling capacitance by 13.05%.

Wanli Yang, Fei Sun, Jiajun Tan, Xinyu Ma, Qi Cao, Dawei Yin, Huawei Shen, Xueqi Cheng. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Promoting positive mental health and well-being, especially in adolescents, is a critical yet underexplored area in natural language processing (NLP). Most existing NLP research focuses on clinical therapy or psychological counseling for the general population, which does not adequately address the preventative and growth-oriented needs of adolescents. In this paper, we introduce DeepWell-Adol, a domain-specific Chinese dialogue corpus grounded in positive psychology and coaching, designed to foster adolescents’ positive mental health and well-being. To balance the trade-offs between data quality, quantity, and scenario diversity, the corpus comprises two main components: human expert-written seed data (ensuring professional quality) and its mirrored expansion (automatically generated using a two-stage scenario-based augmentation framework). This approach enables large-scale data creation while maintaining domain relevance and reliability. Comprehensive evaluations demonstrate that the corpus meets general standards for psychological dialogue and emotional support, while also showing superior performance across multiple models in promoting positive psychological processes, character strengths, interpersonal relationships, and healthy behaviors. Moreover, the framework proposed for building and evaluating DeepWell-Adol offers a flexible and scalable method for developing domain-specific datasets. It significantly enhances automation and reduces development costs without compromising professional standards—an essential consideration in sensitive areas like adolescent and elderly mental health. We make our dataset publicly available.

Generative adversarial network (GAN) is widely used for generalized and robust learning on graph data. However, for non-Euclidean graph data, the existing GAN-based graph representation methods generate negative samples by random walk or traverse in discrete space, leading to the information loss of topological properties (e.g. hierarchy and circularity). Moreover, due to the topological heterogeneity (i.e., different densities across the graph structure) of graph data, they suffer from serious topological distortion problems. In this paper, we proposed a novel Curvature Graph Generative Adversarial Networks method, named CurvGAN, which is the first GAN-based graph representation method in the Riemannian geometric manifold. To better preserve the topological properties, we approximate the discrete structure as a continuous Riemannian geometric manifold and generate negative samples efficiently from the wrapped normal distribution. To deal with the topological heterogeneity, we leverage the Ricci curvature for local structures with different topological properties, obtaining to low-distortion representations. Extensive experiments show that CurvGAN consistently and significantly outperforms the state-of-the-art methods across multiple tasks and shows superior robustness and generalization.

Graph Neural Networks (GNNs) have been widely studied in various graph data mining tasks. Most existing GNNs embed graph data into Euclidean space and thus are less effective to capture the ubiquitous hierarchical structures in real-world networks. Hyperbolic Graph Neural Networks (HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning. In hyperbolic geometry, the graph hierarchical structure can be reflected by the curvatures of the hyperbolic space, and different curvatures can model different hierarchical structures of a graph. However, most existing HGNNs manually set the curvature to a fixed value for simplicity, which achieves a suboptimal performance of graph learning due to the complex and diverse hierarchical structures of the graphs. To resolve this problem, we propose an Adaptive Curvature Exploration Hyperbolic Graph Neural Network named ACE-HGNN to adaptively learn the optimal curvature according to the input graph and downstream tasks. Specifically, ACE-HGNN exploits a multi-agent reinforcement learning framework and contains two agents, ACE-Agent and HGNN-Agent for learning the curvature and node representations, respectively. The two agents are updated by a Nash Q-leaning algorithm collaboratively, seeking the optimal hyperbolic space indexed by the curvature. Extensive experiments on multiple real-world graph datasets demonstrate a significant and consistent performance improvement in model quality with competitive performance and good generalization ability.

In this paper, a linearized radio-over-fiber (RoF) link is proposed. The suppression for the third-order intermodulation distortion (IMD3) and periodic power fading can be simultaneously realized in the proposed link. Adjustable carrier-to-sideband ratio (CSR) can also be achieved. Compared with the traditional RoF link, the spurious free dynamic range (SFDR) of the proposed link is improved by 19 dB.