Skill UI

This skill guides authors on aligning their research manuscript with the scope and standards of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). It provides comprehensive advice on topic fit (e.g., embodied systems, perception, manipulation), structuring the argument for conference reviewers, and detailing necessary evidence (e.g., hardware, simulation, failure cases, system integration). It serves as a strategic tool for advanced robotics conference submission planning.

This guide assists authors targeting the IEEE Spoken Language Technology Workshop (SLT) with conference positioning and manuscript re-framing. It provides deep insights into the required scope, evidence bar (e.g., speech-specific deployment, acoustic conditions), and narrative structure needed to successfully submit research in spoken language understanding, dialogue, and speech systems to top-tier CS venues.

A comprehensive guide for authors writing in computer vision, pattern recognition, or machine learning methods. This skill helps authors evaluate whether their manuscript meets the high methodological rigor, archival standards, and specific formatting requirements of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). It covers scope fit, evidence bar, structure, and pre-submission checklists.

This guide provides rigorous methodology for authors submitting security and machine learning research to top conferences. It emphasizes the need to 'freeze the world' of measurement by pinning target versions, microarchitectural states, and network conditions. It details how to report probabilistic evidence (like attack success rates) and manage ethical data release, ensuring findings remain valid and verifiable over time.

This guide provides crucial strategic advice for authors responding to major AI conference reviews (e.g., IJCAI/IJCAI-ECAI). It emphasizes extreme conciseness, focusing only on decision-critical corrections, factual errors, and pressing questions. The rules strictly prohibit adding new results or broad complaints, ensuring the response remains short and highly targeted.

This guide provides comprehensive standards for designing, auditing, and documenting AI experiments for major conferences like IJCAI. It covers establishing strong baselines, conducting ablation studies, and reporting meticulous details on hyperparameters, computational infrastructure, and ethical risks, ensuring maximum reproducibility and academic credibility for AI research.

Provides comprehensive guidelines for authors submitting to major AI conferences. It helps authors audit originality, properly cite prior art (including preprints, workshop papers, and rejected versions), manage dual-submission risks, and transform the related work section from a mere list into a compelling, novel argument that clearly positions the contribution.

This guide provides detailed best practices for ensuring the reproducibility of advanced AI research across various domains, including algorithms, theories, multi-agent systems, and datasets. It outlines the specific evidence required—from pseudocode and seeds to full proofs and public datasets—to elevate a submission's credibility rating from 'irreproducible' to 'convincing' for top-tier AI conferences.

This guide details the complex, multi-stage review process for top-tier AI conferences like IJCAI. It covers strategies for Phase 1 summary rejection, managing conflicts of interest, understanding the roles of decision actors (PC, AC, SAC), and optimizing paper clarity to maximize acceptance chances. Use this when planning a submission strategy or interpreting reviewer feedback.

A comprehensive checklist for auditing and preparing main-track conference submissions for top-tier AI venues like IJCAI. This guide covers critical compliance points, including double-blind anonymity enforcement, strict page limits, LLM usage policies, author details verification, and identifying potential desk-reject risks before final submission.

This guide offers detailed guidelines for assembling and auditing supplementary materials for top-tier AI conferences (e.g., IJCAI). It covers critical aspects like separating the technical appendix from resubmission files, ensuring anonymity across all artifacts, maintaining the self-contained nature of the core paper, and managing source code and data for maximum reproducibility.

A comprehensive framework for researchers to assess if their AI research contribution is best suited for a major conference like IJCAI, or if a highly specialized venue (such as NeurIPS, ICML, CVPR, or ACL) would provide a clearer audience. It guides decisions based on technical depth, problem scope, and contribution type.