Skill UI

A comprehensive guide for authors submitting to top-tier journals, detailing how to structure policy implication sections for open economies. It emphasizes shifting the focus from generic domestic policy lists to specific, evidence-based recommendations concerning international mechanisms such as trade tariffs, exchange rate elasticity, capital account management, and global value chains (GVCs).

This guide provides a framework for designing and auditing the experimental rigor of academic papers, particularly in systems and programming languages. It emphasizes the crucial link between a claim type (e.g., 'Faster') and the required evidence type (e.g., benchmarks, user studies, formal proofs). Learn how to establish robust baselines, define clear workload rules, size experiments for conference cycles, and ensure statistical compliance for maximum impact.

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 details the best practices for submitting supplementary material (like full proofs, benchmark data, and source code) for major academic conferences. It instructs authors on strategically placing content to enhance depth while strictly maintaining double-blind anonymity and ensuring perfect consistency between the main paper and its supporting appendices.

A crucial guide for authors preparing for PODC (Proofs venue). It corrects the common misconception that runnable artifacts or badges are required. Instead, it redirects focus to the three core areas of evaluation: ensuring proof completeness, establishing model rigor by declaring all assumptions, and maintaining transparency for optional simulations.

A comprehensive guide for authors on preparing an accepted academic paper for ACM conference proceedings. This includes de-anonymizing the manuscript, compressing the content to the strict 10-page two-column format, migrating full proofs to arXiv, and completing all required ACM metadata (rights, CCS concepts).

This guide outlines the stringent structural requirements for writing papers on distributed systems theory, specifically for top-tier conferences like PODC. It details how to front-load the core arguments, define the model box rigorously (network, timing, fault model), structure proofs logically, and ensure the key results are visible within the critical first 10 pages.

This guide outlines the rigorous standards for academic paper reproducibility, particularly for theoretical venues. It mandates a complete, self-contained claim-to-proof map, ensuring every stated theorem or bound is fully provable within the submitted document, thereby preventing reliance on external or deferred proofs.

This guide details the rigorous process for packaging and evaluating mechanized proofs (e.g., in Coq, Isabelle, Lean, Agda) submitted for academic conferences. It outlines criteria for achieving Functional, Reusable, and Available badges, focusing heavily on completing the formal development, auditing axioms, and ensuring perfect reproducibility by pinning toolchains and mapping theorems to source files.

This guide outlines the rigorous standards for achieving full reproducibility in theoretical computer science papers (like those presented at POPL). It details how to ensure that independent readers can re-establish research claims by meticulously tracking mechanized theorems, on-paper proofs, formal semantics, and empirical results. Key practices include maintaining a central correspondence table and ensuring assumption hygiene to prevent logical divergence.

This guide helps researchers determine the optimal venue (like POPL, PLDI, OOPSLA) for their academic papers on programming languages. It advises authors on whether their core contribution is theoretical (proofs, semantics), implementation-focused (compilers, benchmarks), or empirical (user studies), ensuring their work reaches the most appropriate specialized reviewer community.

Covers best practices for writing highly technical academic papers, particularly in programming language theory. It guides authors on structuring their work to build a strong 'ramp' from initial concrete programs and informal motivation to rigorous formal proofs and theorems, ensuring clarity and avoiding common pitfalls like unmotivated formalism or ambiguous notation.