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Haixiao Li

Publications and source records attributed to Haixiao Li.

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GoGoTB: Agentic RTL Verification with Specification-Grounded Coverage Closure

Functional verification dominates integrated circuit (IC) front-end engineering effort, and a single missed bug that escapes to silicon can trigger a costly respin. Recent large language models (LLMs) offer new opportunities to automate this process, yet existing LLM-based approaches generate each component through independent single-turn calls with no shared context, leaving interface mismatches undetected and reported coverage disconnected from specification requirements. To address these challenges, we present GoGoTB, an agentic framework that achieves end-to-end verification closure through three subsystems: an agentic execution control layer, an evolvable knowledge system, and specification-grounded coverage closure. The execution control layer separates deterministic enforcement from LLM reasoning at every tool and stage boundary. The knowledge system dispatches methodology and design-specific expertise on demand. The coverage framework anchors every bin to a named specification behavior so that each residual gap has a diagnosable root cause and a targeted remedy. Tested on 8 register transfer level (RTL) designs without any human intervention, GoGoTB achieves 100\% environment generation success and averages 98.4\% line, 97.2\% branch, 97.0\% toggle, and 83.2\% functional coverage. No prior work successfully generates a complete verification environment or achieves meaningful coverage on the same benchmarks.

cs.AI

Advanced simulation framework for AC/MTDC power systems

Alternating current (AC)/multi-terminal direct current (MTDC) hybrid power systems (HPSs) play a crucial role in enabling long-distance power transmission and flexible interconnections between AC grids. However, the challenges that HPSs encountered are numerous, with stability and harmonic issues being particularly prominent. Traditional electromagnetic transient (EMT) tools have struggled to accommodate small-signal stability problems and the potential issues of the optimal interactions among converters. To address this gap, HARMONY ("HARMONic stabilitY assessment of PE-penetrated power systems") has been developed for the advanced simulation and analysis of interconnected AC/MTDC HPSs as a comprehensive mathematical framework based on C++ programming language. The primary goals of Harmony are to provide faster and trusted stability analyses, and address the analytical difficulties associated with converter control dynamics, converter-driven stability, and interoperability in HPSs. This framework is intended to be open source, therefore broadening collaboration for researchers, and to contribute to the community of power systems engineers. In this paper, we demonstrate two core functionalities featured in HARMONY, that are optimal power flow (OPF) and harmonic stability analyses (HAS). The underlying analysis models and computational methodologies for both functionalities are presented in detail to help future readers and users gain a clear understanding of mathematical fundamentals of HARMONY. Furthermore, we introduce the integrated framework of OPF and HAS designed in HARMONY, along with representative printed analysis results, to demonstrate the appealing capabilities of HARMONY.

eess.SY

Leveraging Optimal Information-Power Flow for Transmission Switching in AC/MTDC Grids

The emerging AC/multi-terminal DC grids are regarded as a promising solution for accommodating the increasing integration of renewable energy sources. This work proposes an optimization framework to address transmission switching (TS) problems arising in practical operational scenarios, such as maintenance scheduling, contingency management, and fault restoration. Unlike most existing studies, the proposed framework considers the role of communication networks in TS operations and develops an optimal information-power flow (OIPF) model. The OIPF model captures the impact of information flows on circuit breaker actions while incorporating communication-related costs, thereby better reflecting practical operational decision-making processes. To ensure computational tractability, the resulting optimization problem is formulated as a mixed-integer second-order cone programming (MISOCP) model through convex relaxations, polygonal approximations, and Big-M reformulations. Numerical case studies illustrate the applicability of the proposed OIPF model and indicate its potential in supporting transmission switching decisions.

eess.SY

Distributed Robust Optimization Method for AC/MTDC Hybrid Power Systems with DC Network Cognizance

AC/multi-terminal DC (MTDC) hybrid power systems have emerged as a solution for the large-scale and longdistance accommodation of power produced by renewable energy systems (RESs). To ensure the optimal operation of such hybrid power systems, this paper addresses three key issues: system operational flexibility, centralized communication limitations, and RES uncertainties. Accordingly, a specific AC/DC optimal power flow (OPF) model and a distributed robust optimization method are proposed. Firstly, we apply a set of linear approximation and convex relaxation techniques to formulate the mixed-integer convex AC/DC OPF model. This model incorporates the DC network-cognizant constraint and enables DC topology reconfiguration. Next, generalized Benders decomposition (GBD) is employed to provide distributed optimization. Enhanced approaches are incorporated into GBD to achieve parallel computation and asynchronous updating. Additionally, the extreme scenario method (ESM) is embedded into the AC/DC OPF model to provide robust decisions to hedge against RES uncertainties. ESM is further extended to align the GBD procedure. Numerical results are finally presented to validate the effectiveness of our proposed method.

math.OC