The recent developments in the research area demonstrate a significant shift towards leveraging advanced computational models and hardware accelerators to tackle complex problems. A notable trend is the integration of neuromorphic and quantum hardware with traditional computational methods, exemplified by the structure-aware reductions and tight lower bounds established for MaxSAT, Max2SAT, and QUBO problems. This approach not only optimizes the efficiency of hardware accelerators but also provides new insights into the structural properties of these problems. Additionally, there is a growing interest in the theoretical underpinnings of higher-order Ising models and their simulation, which could pave the way for scalable solutions to industrial-level problems. The field is also witnessing advancements in model counting, particularly in the context of Pseudo-Boolean formulas, where new counters are being developed to support projected and incremental settings. Furthermore, the use of dynamical systems for solving NP-complete problems, as demonstrated by the accurate modeling of continuous-time SAT solvers in SPICE, opens up new avenues for research in this domain. Lastly, the development of tools like Cirbo for Boolean circuit analysis and synthesis highlights the continuous effort to optimize and innovate in computational logic.

Noteworthy papers include one that establishes structure-aware reductions between MaxSAT, Max2SAT, and QUBO, providing tight lower bounds and new algorithms, and another that introduces a novel framework for simulating higher-order Ising models, offering significant speed improvements.