Skill UI

This guide provides authors with comprehensive best practices for submitting research artifacts (e.g., compilers, benchmarks, proofs) for post-acceptance evaluation at PLDI. It details how to structure the submission, ensuring reproducibility, containerization, and clear documentation to achieve Functional, Reusable, or Available badges.

This guide provides best practices for designing and auditing academic evaluations of programming language features and compilers, following standards used in venues like PLDI. It covers selecting defensible benchmark suites, establishing strong baselines, performing targeted ablations, and measuring multiple performance metrics (runtime, compile time, memory) to ensure research claims are reproducible and scientifically sound.

This guide addresses the stringent requirements for empirical evaluation in compiler and runtime systems, aligning with SIGPLAN's guidelines. It covers critical aspects such as proper warmup discipline, reporting variance and confidence intervals, selecting principled benchmarks, pinning toolchains, and ensuring cross-platform validity. It provides a protocol for generating verifiable measurement logs for academic publications.

This guide provides a detailed framework for researchers to determine the most suitable venue (e.g., PLDI, POPL, OOPSLA) for their programming language or systems research paper. It guides authors on whether their core evidence lies in theoretical proofs, working implementations with benchmarks, or empirical usage studies, ensuring optimal submission routing and maximizing acceptance chances.

A comprehensive guide for authors submitting technical papers to top compiler and systems conferences (like PLDI). It teaches how to structure an argument as a rigorous engineering case, focusing on stating the 'hard case' first, describing mechanisms rather than using adjectives, and ensuring every claim is directly backed by evidence throughout the paper.

This guide outlines the rigorous standards for presenting 'evidence' in highly theoretical computing venues. It emphasizes that results must be supported by deep mathematical proofs, establishing matching upper and lower bounds (tightness), and meticulously auditing all underlying model assumptions (e.g., timing, failure modes). It shifts focus from empirical benchmarks to formal correctness.

This comprehensive guide provides a rigorous framework for auditing the technical and mathematical evidence in theoretical computer science papers. It instructs authors on how to ensure that every claim—especially those regarding optimality or complexity bounds—is supported by matching lower and upper bounds, explicit assumptions, and consistent cost models. It emphasizes that the evidence must be robust proofs, not merely empirical benchmarks.

A comprehensive guide for structuring the analysis and results section of academic papers in Production and Operations Management (POM). It covers rigorous theoretical proofs, advanced empirical identification methods (e.g., DiD, IV), and data science validation techniques. The core focus is bridging statistical significance with actionable, operational managerial magnitude, ensuring research findings are decision-relevant.

This guide assists researchers in selecting the most appropriate and rigorous methodology for Production and Operations Management (POM) manuscripts. It covers various research tracks, including analytical modeling, stochastic process theory, game theory, causal empirical identification (DiD, IV, RD), behavioral experiments, simulation, and operations data science (ML/forecasting). It ensures the research method is tightly linked to the core operational question and possesses significant practical value.

A comprehensive guide for designing and presenting empirical evidence (tables and figures) in Production and Operations Management (POM) manuscripts. It helps authors manage the limited 'exhibit budget' across the main paper and the online e-companion, ensuring that all visuals are decision-legible, operationally relevant, and adhere to journal standards.

Guides the rigorous development of theoretical models and research frameworks for Production and Operations Management (POM) manuscripts. It covers analytical, empirical, and behavioral tracks, ensuring that all assumptions and mechanisms are deeply grounded in real-world operational constraints and decision-making processes, thereby building a defensible academic argument.

This skill guides users on elevating empirical findings (raw correlations) into robust, generalizable, and theoretically grounded arguments suitable for high-impact academic journals. It teaches how to explicitly define mechanisms, scope conditions, and observable implications to transform descriptive work into conceptual, mechanistic contributions.