Skill UI

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.

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.

Provides detailed guidelines for submitting empirical papers to POPL. It teaches authors how to correctly match specific claims (e.g., soundness, precision) with appropriate evidence types (proofs, case studies, effort data). The core principle emphasized is that evidence must demonstrate realizability but never substitute for the formal theorem.

This comprehensive guide outlines the rigorous methodology required for designing high-impact evaluations of parallel systems and algorithms for top-tier conferences. It stresses that contributions must demonstrate the 'twin bar': both concurrency correctness (e.g., linearizability) and measurable scalability (e.g., strong vs. weak scaling). It covers best practices for establishing strong baselines, managing measurement hygiene (variance, pinning), and anticipating advanced reviewer scrutiny in HPC.

Provides a comprehensive guide for writing high-quality academic rebuttal letters and revised manuscripts, especially for top-tier journals. It covers structuring responses to detailed reviewer comments, addressing significant revisions, incorporating new experiments, and maintaining professional, evidence-based communication to maximize acceptance chances.

This guide provides a comprehensive checklist for designing, executing, and presenting empirical evidence for academic papers in Pattern Recognition and AI. It covers critical areas like establishing fair baselines, ensuring statistical rigor (mean ± std dev, significance testing), conducting sufficient ablation studies, preventing data leakage, and reporting computational costs. It helps authors make their findings withstand rigorous external review.

This guide outlines comprehensive best practices for ensuring the reproducibility of Machine Learning and Pattern Recognition research. It covers fixing random seeds, locking hardware/software dependencies (Python, CUDA, etc.), managing data splits, tracking pre-trained model versions, and structuring the required artifact package for external review. It is essential for building trust in published scientific results.

Provides comprehensive guidelines for authors submitting long-form research papers to academic journals like PR&AI. It teaches how to strategically segment content, ensuring core arguments reside in the main text while detailed proofs, extra experiments, and supplementary data are moved to appendices, maintaining academic rigor and readability.

This guide helps authors determine if their manuscript is suitable for submission to Progress in Aerospace Sciences. It emphasizes that the journal requires comprehensive, critical review articles that synthesize existing knowledge, rather than presenting original primary research, simulations, or experiments. Use this tool for fit assessment, scope definition, and structural re-framing before submission.

This skill provides a comprehensive framework for assessing whether an economics manuscript is suitable for the Quarterly Journal of Economics (QJE). It guides authors through evaluating the research scope (must be general-interest), evidence quality (requires clean natural experiments or quasi-experimental designs), narrative structure (leading with consequential findings), and adherence to rigorous academic submission standards.

A comprehensive framework for designing, auditing, and reporting empirical results in recommender systems research. It covers best practices for handling temporal splits, mitigating evaluation bias (e.g., exposure/popularity), utilizing advanced off-policy estimators (IPS, SNIPS), and structuring the critical link between offline metrics and real-world A/B test results. Essential for academic submissions.

This guide provides a comprehensive strategy for elevating the credibility of finance manuscripts to the standard of top-tier journals. It details rigorous methods for empirical causal identification (DID, IV, RDD, natural experiments) and ensures theoretical rigor by assessing assumptions, generalizability, and proof exposition. It helps researchers stress-test their work's core logic.