1% generated by tables/make_tables.py from ./literature_synthesis/agent_G/contributions.csv2\begin{longtable}{lrrrrr}3\caption{Landmark candidates of the theory and software areas retained as in-scope research or survey contributions, with the contribution type judged from the abstract or metadata.}\label{tab:theory_landmarks_discussed}\\4\toprule5\textbf{Category} & \textbf{Year} & \textbf{Type} & \textbf{Work} & \textbf{Contribution} & \textbf{Id mismatch} \\6\midrule7\endfirsthead8\toprule9\textbf{Category} & \textbf{Year} & \textbf{Type} & \textbf{Work} & \textbf{Contribution} & \textbf{Id mismatch} \\10\midrule11\endhead12Algo. & 2022 & article & A Dynamic Survey of Graph Labeling & survey & no \\13Algo. & 2024 & article & Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks & engineering & yes \\14Algo. & 2025 & conf. & Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Dive\dots & method & yes \\15Algo. & 2026 & article & Multiresource-Constrained Flexible Job Shop Scheduling With Fixture Pallets and Setup\dots & method & no \\16Comp. & 2018 & article & Machine Learning in Compiler Optimization & survey & no \\17Comp. & 2018 & article & A Survey on Compiler Autotuning using Machine Learning & survey & no \\18Comp. & 2018 & chapter & Automated Code Generation for Maximizing Performance of Detailed Chemistry Calculatio\dots & engineering & no \\19Comp. & 2019 & conf. & Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code & engineering & no \\20Comp. & 2020 & chapter & ExaStencils: Advanced Multigrid Solver Generation & engineering & no \\21Comp. & 2021 & conf. & MLIR: Scaling Compiler Infrastructure for Domain Specific Computation & engineering & no \\22Comp. & 2024 & article & LLM4VV: Developing LLM-driven testsuite for compiler validation & benchmark & no \\23Comp. & 2025 & conf. & LLM Compiler: Foundation Language Models for Compiler Optimization & engineering & no \\24Compl. & 2019 & conf. & The Reachability Problem for Petri Nets Is Not Elementary & theory & no \\25Compl. & 2026 & article & Solving SAT by restricted spiking neural P systems with temporal division rules & theory & no \\26FV & 2018 & preprint & Efficient Formal Safety Analysis of Neural Networks & method & no \\27FV & 2019 & conf. & The Marabou Framework for Verification and Analysis of Deep Neural Networks & engineering & no \\28FV & 2019 & article & An abstract domain for certifying neural networks & method & no \\29FV & 2020 & conf. & VerX: Safety Verification of Smart Contracts & method & no \\30FV & 2022 & conf. & cvc5: A Versatile and Industrial-Strength SMT Solver & engineering & no \\31FV & 2023 & article & Exploiting Generative AI to Scale up Intelligent Tutoring Systems & engineering & yes \\32FV & 2024 & preprint & Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Exp\dots & method & yes \\33FV & 2024 & conf. & State of the Art in Software Verification and Witness Validation: SV-COMP 2024 & benchmark & no \\34FV & 2026 & article & Reward Interfaces with Best-Effort Implementations & theory & no \\35PL & 2018 & conf. & ZEUS: Analyzing Safety of Smart Contracts & method & no \\36PL & 2024 & article & Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach & method & no \\37PL & 2024 & article & WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models & method & no \\38SE & 2018 & article & Engenharia de Software & survey & no \\39SE & 2018 & chapter & Mutation Testing Advances: An Analysis and Survey & survey & no \\40SE & 2020 & article & Machine Learning Testing: Survey, Landscapes and Horizons & survey & no \\41SE & 2021 & preprint & A Direction-Aware Study of LLM-Based Code Translation Across Eight Programming Languages & empirical & yes \\42SE & 2022 & article & The Astropy Project : Sustaining and Growing a Community-oriented Open-source Project\dots & engineering & no \\43SE & 2024 & article & Software Testing With Large Language Models: Survey, Landscape, and Vision & survey & no \\44SE & 2025 & conf. & Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functional\dots & method & yes \\45SE & 2025 & conf. & On Assessing ML Model Robustness: A Methodological Framework (Academic Track) & engineering & yes \\46SE & 2025 & article & Can Open Large Language Models Catch Vulnerabilities? & empirical & no \\47SE & 2026 & article & The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experime\dots & empirical & no \\48SE & 2026 & article & MSTDP: a multi-scale temporal deep learning framework for just-in-time software defec\dots & method & no \\49\bottomrule50\multicolumn{6}{p{0.95\linewidth}}{\footnotesize Contribution types follow the protocol (theory, method, empirical, benchmark, engineering, survey). `Id mismatch' as in Table~\ref{tab:theory_landmark_audit}. Titles are shortened.}\\51\end{longtable}52