Skill UI

This skill simulates the rigorous peer review process used in top-tier AI and machine learning conferences (like NeurIPS). It helps users diagnose potential weaknesses in a submission regarding quality, clarity, significance, ethical compliance, and reproducibility. It guides the identification of decision-relevant issues that ACs and reviewers are likely to raise, helping authors strategically plan responses for revision.

This skill guides researchers through the rigorous validation process of their AI/ML paper for NeurIPS. It helps determine if the core contribution is robust enough for the main track, suggests the most appropriate NeurIPS track (e.g., E&D, Position), and refines the contribution type (Theory, Use-Inspired, etc.) to maximize the chances of acceptance.

A comprehensive guide for authors targeting the NAACL conference. It helps strategize the fit, framing, and rigor required for NLP and Computational Linguistics manuscripts. Use this to structure your paper, understand the evidence bar, and position your work for a top-tier conference audience.

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 assists researchers in determining the topic suitability, appropriate format (research paper, review, special issue), and correct academic placement for manuscripts targeting 'Pattern Recognition and Artificial Intelligence' (PR&AI). It outlines the journal's focused scope across core AI domains like ML, CV, and NLP, helping authors avoid common submission pitfalls and ensuring their contribution has sufficient originality and academic depth.

A comprehensive guide for revising academic papers in recommendation systems (RecSys). It covers best practices for structuring the first page, ensuring rigorous evaluation protocols (e.g., temporal splits, equal-budget baselines), and avoiding methodological overclaims, helping authors meet the standards of top-tier AI conferences.

This comprehensive guide helps authors determine if their robotics manuscript is suitable for the RoboCup Symposium. It provides specialized advice on conference positioning, required evidence (e.g., league performance, benchmarks), and framing for competitive robotics domains like robot soccer, rescue, and service robots. It emphasizes grounding the contribution in empirical, competition-based evidence rather than general theoretical work, serving as a fit and re-framing tool for high-level CI/AI submissions.

This comprehensive guide helps authors determine the suitability and optimal presentation of their robotics scientific manuscripts for the Robotics: Science and Systems (RSS) conference. It provides strategic advice on conference positioning, defining the evidence bar, re-framing algorithmic or theoretical contributions, and addressing the specific needs of top-tier embodied systems reviewers.

A comprehensive strategic guide for authors submitting to the SIGDIAL Conference on Discourse and Dialogue. It helps evaluate if a dialogue system manuscript is suitable for the venue, refining the paper's narrative, strengthening empirical evidence, and understanding the specific requirements for discourse and conversational AI research to maximize submission success.

This skill provides a comprehensive guide for manuscript authors targeting the StarSem Conference on Computational Semantics (*SEM). It helps authors determine the conference fit, refine framing, establish the necessary evidence bar, and strategize submissions for topics related to meaning representation, semantic parsing, and inference. It is a specialized venue-selection and re-framing tool for advanced NLP/CS papers.

This skill guides authors on positioning foundational and conceptually general robotics research for submission to The International Journal of Robotics Research (IJRR). It evaluates the manuscript's depth, generality, and conceptual framework, helping authors re-frame single-demonstration results into field-advancing principles. It also assists in choosing between IJRR and competing venues like IEEE Transactions on Robotics (T-RO).

This guide helps researchers determine the optimal venue for their Computer Vision paper, specifically addressing the unique identity of WACV (Applications-first) versus major conferences like CVPR, ICCV, and ECCV. It guides authors on whether their contribution is driven by real-world applications/constraints or by advancing core algorithms, and how to choose between the Applications or Algorithms track within WACV.