% generated by tables/make_tables.py from ./literature_synthesis/agent_G/contributions.csv
\begin{longtable}{lrrrrr}
\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}\\
\toprule
\textbf{Category} & \textbf{Year} & \textbf{Type} & \textbf{Work} & \textbf{Contribution} & \textbf{Id mismatch} \\
\midrule
\endfirsthead
\toprule
\textbf{Category} & \textbf{Year} & \textbf{Type} & \textbf{Work} & \textbf{Contribution} & \textbf{Id mismatch} \\
\midrule
\endhead
Algo. & 2022 & article & A Dynamic Survey of Graph Labeling & survey & no \\
Algo. & 2024 & article & Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks & engineering & yes \\
Algo. & 2025 & conf. & Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Dive\dots & method & yes \\
Algo. & 2026 & article & Multiresource-Constrained Flexible Job Shop Scheduling With Fixture Pallets and Setup\dots & method & no \\
Comp. & 2018 & article & Machine Learning in Compiler Optimization & survey & no \\
Comp. & 2018 & article & A Survey on Compiler Autotuning using Machine Learning & survey & no \\
Comp. & 2018 & chapter & Automated Code Generation for Maximizing Performance of Detailed Chemistry Calculatio\dots & engineering & no \\
Comp. & 2019 & conf. & Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code & engineering & no \\
Comp. & 2020 & chapter & ExaStencils: Advanced Multigrid Solver Generation & engineering & no \\
Comp. & 2021 & conf. & MLIR: Scaling Compiler Infrastructure for Domain Specific Computation & engineering & no \\
Comp. & 2024 & article & LLM4VV: Developing LLM-driven testsuite for compiler validation & benchmark & no \\
Comp. & 2025 & conf. & LLM Compiler: Foundation Language Models for Compiler Optimization & engineering & no \\
Compl. & 2019 & conf. & The Reachability Problem for Petri Nets Is Not Elementary & theory & no \\
Compl. & 2026 & article & Solving SAT by restricted spiking neural P systems with temporal division rules & theory & no \\
FV & 2018 & preprint & Efficient Formal Safety Analysis of Neural Networks & method & no \\
FV & 2019 & conf. & The Marabou Framework for Verification and Analysis of Deep Neural Networks & engineering & no \\
FV & 2019 & article & An abstract domain for certifying neural networks & method & no \\
FV & 2020 & conf. & VerX: Safety Verification of Smart Contracts & method & no \\
FV & 2022 & conf. & cvc5: A Versatile and Industrial-Strength SMT Solver & engineering & no \\
FV & 2023 & article & Exploiting Generative AI to Scale up Intelligent Tutoring Systems & engineering & yes \\
FV & 2024 & preprint & Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Exp\dots & method & yes \\
FV & 2024 & conf. & State of the Art in Software Verification and Witness Validation: SV-COMP 2024 & benchmark & no \\
FV & 2026 & article & Reward Interfaces with Best-Effort Implementations & theory & no \\
PL & 2018 & conf. & ZEUS: Analyzing Safety of Smart Contracts & method & no \\
PL & 2024 & article & Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach & method & no \\
PL & 2024 & article & WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models & method & no \\
SE & 2018 & article & Engenharia de Software & survey & no \\
SE & 2018 & chapter & Mutation Testing Advances: An Analysis and Survey & survey & no \\
SE & 2020 & article & Machine Learning Testing: Survey, Landscapes and Horizons & survey & no \\
SE & 2021 & preprint & A Direction-Aware Study of LLM-Based Code Translation Across Eight Programming Languages & empirical & yes \\
SE & 2022 & article & The Astropy Project : Sustaining and Growing a Community-oriented Open-source Project\dots & engineering & no \\
SE & 2024 & article & Software Testing With Large Language Models: Survey, Landscape, and Vision & survey & no \\
SE & 2025 & conf. & Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functional\dots & method & yes \\
SE & 2025 & conf. & On Assessing ML Model Robustness: A Methodological Framework (Academic Track) & engineering & yes \\
SE & 2025 & article & Can Open Large Language Models Catch Vulnerabilities? & empirical & no \\
SE & 2026 & article & The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experime\dots & empirical & no \\
SE & 2026 & article & MSTDP: a multi-scale temporal deep learning framework for just-in-time software defec\dots & method & no \\
\bottomrule
\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.}\\
\end{longtable}