Skill UI

This comprehensive guide assists researchers in converting an accepted, anonymous draft for the Conference on Learning Theory (COLT) into the final, polished camera-ready version for publication in PMLR proceedings. It covers critical steps such as de-anonymization (restoring author details, acknowledgements), consolidating all promised revisions into the main text, verifying PMLR metadata and BibTeX consistency, and preparing the full archival PDF.

This skill provides comprehensive guidance on positioning, structuring, and refining computer vision manuscripts for top-tier venues like CVPR. It covers identifying major novelty, required experimental rigor (e.g., ablations, failure modes), framing contributions, and adhering to specific conference submission standards for CV, VLM, and recognition tasks. Use it to refine your submission strategy and ensure your work meets the high bar set by leading AI conferences.

A comprehensive strategic guide for authors targeting the Conference and Labs of the Evaluation Forum (CLEF). It helps frame IR/NLP research by defining the appropriate contribution type, evidence bar, and rigorous evaluation methods required for top-tier academic submission, ensuring the paper aligns with the venue's specific community expectations.

This skill guides authors on how to position, structure, and strengthen empirical Natural Language Processing (NLP) manuscripts for submission to the Conference on Empirical Methods in Natural Language Processing (EMNLP). It provides deep insights into conference fit, required evidence (baselines, error analysis, datasets), proper framing, and anticipating reviewer expectations for top-tier AI venues.

A comprehensive guide for positioning continual learning and lifelong agent research for the Conference on Lifelong Learning Agents (CoLLAs). This tool helps authors strategically re-frame their work, assess its fit, and optimize the narrative to meet the specific evidence bar of top CS venues focusing on nonstationary learning, adaptation, and memory. It covers scope calibration, required evidence types, and comparison with related ML conferences.

This guide helps authors position their machine learning systems manuscripts for the rigorous Conference on Machine Learning and Systems (MLSys). It details the venue's specific focus on system-level contributions, including training/inference infrastructure, compilers, data systems, and hardware-aware ML. Learn how to frame your work to appeal to systems reviewers by emphasizing measurable impacts on capability, cost, reliability, or deployment scale.

A comprehensive pre-upload audit checklist for submitting papers to the CoRL (Conference on Robot Learning). This guide details specific requirements for the submission package, including the mandatory 8-page limit (which must account for a dedicated Limitations section), adherence to double-anonymous rules, and detailed sweeps for preserving author and lab anonymity in text, figures, and videos. Essential for ensuring compliance with complex conference guidelines.

A comprehensive guide detailing the submission rules and best practices for supplementary materials, including videos, appendices, code, and data, for top-tier robotics conferences. It outlines distinct submission channels, strict technical requirements (e.g., 250MB video cap, anonymity), and the necessary transition steps from the review phase to the final camera-ready publication format.

This guide helps researchers determine if their embodied AI or robotics project is best suited for the Conference on Robot Learning (CoRL). It provides a rigorous two-axis test—assessing if learning is the core contribution and if embodiment is essential to the claim—to decide between submitting to CoRL, ICRA, IROS, NeurIPS, or other journals.

A specialized routing tool designed for academic researchers submitting manuscripts to top-tier Computer Science and AI conferences. It helps users navigate complex decision points, such as selecting the best venue (e.g., ICML vs NeurIPS), matching contribution types (theory, system, empirical study), and adhering to specific submission guidelines and review constraints.

A comprehensive guide for authors submitting to computer vision conferences, detailing how to structure, organize, and utilize supplementary material. It clearly distinguishes between core claims (which must be in the main body) and supportive evidence, such as full ablation grids, detailed derivations, and video results, which belong in the supplement. The guide covers best practices for handling anonymity, video evidence, and avoiding common submission bugs.

A comprehensive project management skill designed for authors preparing papers for the EACL conference. It maps out the entire single-cycle submission process, covering backward planning from the ARR deadline, rebuttal strategies, commitment decisions, and final camera-ready stages. Essential for navigating highly structured academic publication timelines and minimizing risk.