Skill UI

A comprehensive guide detailing the strict requirements for submitting research papers to EMNLP via the ACL Rolling Review (ARR). Key areas covered include template compliance, page length restrictions (long/short format), mandatory limitations sections, double-blind anonymization techniques, and adherence to the Responsible NLP checklist. Use this audit guide before submission to ensure full compliance and avoid desk-rejects.

This guide outlines best practices for structuring advanced NLP/AI research papers for academic submission. It details the critical differences between core content pages, mandatory limitations sections, discretionary appendices, and supplementary uploads, ensuring that reviewers are properly guided and that the work remains reproducible and verifiable.

A comprehensive guide for structuring the "Related Work" section for top-tier computer vision conferences (ICCV, CVPR, ECCV). It covers best practices for citing intervening-cycle literature, managing concurrent preprints, ensuring double-blind anonymity, and correctly attributing source venues to maximize novelty perception and score highly with reviewers.

A comprehensive guide for authors preparing the 'Related Work' section for top-tier robotics conferences like ICRA. It details how to conduct thorough literature reviews across multiple research 'lanes,' handle concurrent work (arXiv), properly self-cite under double-anonymous rules, and position the novelty against both academic rivals and commercial systems.

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.

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 comprehensive guide outlines the strict technical and structural requirements for the IROS supplementary video, which serves as primary evidence for submitted research. It details constraints such as the 60-second limit, 10MB size cap, and necessary formats. The guide emphasizes presenting uncut real trials, including labeled failures, maintaining honest playback speed, and performing thorough anonymization to maximize academic credibility.

A comprehensive guide for writing the 'Related Work' section for top-tier speech conference papers. It teaches authors how to trace the academic lineage of a task across multiple venues (e.g., ISCA, ICASSP, NeurIPS), properly cite canonical corpora and metrics, and condense complex literature reviews into concise, contrastive clauses.

This guide teaches how to structure the "Related Work" section for top-tier AI journals, focusing on demonstrating the novel incremental contribution (delta) rather than simply listing existing literature. It covers positioning the work within ML/CV/NLP, defining novel problem dimensions, and structuring rigorous comparison tables to withstand external review scrutiny.

A comprehensive guide for structuring the Related Work section for top-tier recommender systems conferences (RecSys). It advises authors on auditing novelty by positioning their contribution against adjacent research fields (e.g., SIGIR, KDD, WSDM), addressing critical lines (like reproducibility and off-policy evaluation), and ensuring the work is technically distinct from prior or concurrent studies.

A comprehensive guide for structuring the 'Related Work' section in academic papers, especially in Computer Vision (CV). It teaches authors how to strategically position their research contribution by covering multiple literature shelves: vision methods, domain constraints, concurrent arXiv work, and prior art. This ensures the paper clearly defines a unique gap rather than merely listing citations, adhering to double-blind review standards.

A specialized downstream skill for analyzing security vulnerabilities in complex agentic systems. This tool is designed for red teaming and penetration testing, focusing on detecting prompt injection, retrieval poisoning, memory contamination, planner drift, and tool boundary abuse within multi-step LLM chains. Use when proving how untrusted inputs compromise system integrity.