Jingren Wang

Jingren Wang, Guangyu Hu, Shiju Lin et al.

And-Inverter Graph (AIG)-based logic synthesis has been a cornerstone of digital design automation for several decades. While numerous optimization techniques have been developed for both technology-independent and technology-dependent synthesis stages, existing technology mapping approaches predominantly employ graph-covering strategies directly on AIG representations without adequately addressing complemented edge distribution. Neglecting inverters creates a significant disconnect: complemented edges are systematically overlooked in technology-independent cost functions, yet they abruptly become critical during technology-dependent mapping. In this work, we introduce a delay-driven pre-processing stage that operates prior to technology mapping, designed to strategically redistribute complemented edges and mitigate the inverter-induced costs on critical paths. Experimental validation demonstrates that our delay-targeted methodology not only preserves original delay characteristics but also enables performance improvements. Notably, arithmetic logic in the EPFL combinational benchmark exhibits particular sensitivity to this approach, with our method achieving an average delay reduction of 0.49% and a maximum improvement of 3.86% on the case sqrt.

Elise Karinshak, Amanda Hu, Kewen Kong et al.

Immense effort has been dedicated to minimizing the presence of harmful or biased generative content and better aligning AI output to human intention; however, research investigating the cultural values of LLMs is still in very early stages. Cultural values underpin how societies operate, providing profound insights into the norms, priorities, and decision making of their members. In recognition of this need for further research, we draw upon cultural psychology theory and the empirically-validated GLOBE framework to propose the LLM-GLOBE benchmark for evaluating the cultural value systems of LLMs, and we then leverage the benchmark to compare the values of Chinese and US LLMs. Our methodology includes a novel "LLMs-as-a-Jury" pipeline which automates the evaluation of open-ended content to enable large-scale analysis at a conceptual level. Results clarify similarities and differences that exist between Eastern and Western cultural value systems and suggest that open-generation tasks represent a more promising direction for evaluation of cultural values. We interpret the implications of this research for subsequent model development, evaluation, and deployment efforts as they relate to LLMs, AI cultural alignment more broadly, and the influence of AI cultural value systems on human-AI collaboration outcomes.