Skill UI

A comprehensive guide for authors aiming to submit machine learning manuscripts to the Asian Conference on Machine Learning (ACML). This skill helps evaluate manuscript fit, re-frame research from journal or arXiv styles, understand required contribution types (e.g., algorithms, theorems, benchmarks), and align the paper's evidence bar with the expectations of a top-tier, regional AI/ML venue. Use it for venue selection and submission strategy development.

This guide details how to read, interpret, and synthesize artifacts from the central shared workspace, known as the 'bench.' It provides a structured approach to gathering findings, reports, and assessments left by various modules (desks). Use this skill to build comprehensive context, understand cross-desk dependencies, and summarize complex information for an operator.

This guide outlines the comprehensive process for submitting and evaluating Computer Aided Verification (CAV) research artifacts. It details the criteria for earning three badges (Available, Functional, Reusable), the two-phase review structure (smoke-test and full review), and best practices for packaging scientific tools, including the use of containers, pinned benchmarks, resource limits, and proof witnesses. This ensures reproducibility and rigor in academic research.

A comprehensive guide for drafting a technical author response (rebuttal) for peer-reviewed academic papers (CAV). It teaches authors how to structure a rebuttal by addressing specific objections related to proof soundness, benchmark fairness, novelty, and scope, using only verifiable evidence, not future promises.

This guide outlines best practices for designing and executing Computer Aided Verification (CAV) empirical evaluations. It emphasizes ensuring evidence matches claims by using standard, pinned benchmark sets, selecting fair baselines, respecting equal resource limits, and employing rigorous reporting methods like cactus and scatter plots to establish technical soundness and performance.

Provides detailed guidelines for authors on how to accurately position their research novelty within the complex landscape of formal verification techniques, tools, and academic literature. It emphasizes shifting from a simple citation catalog to a 'delta-first' contrast, clearly articulating technical differences, crediting specific tool and benchmark lineages, and understanding the scope of related venues (e.g., CAV, TACAS, FMCAD).

This guide provides detailed protocols for achieving high levels of reproducibility in Computer Aided Verification (CAV) research. It emphasizes the necessity of pinning benchmark sets, recording exact tool versions, documenting resource limits (time/memory), and providing checkable proof witnesses to ensure that evaluation claims can be independently validated by competitors or reviewers.

Provides strict guidelines for assembling supplementary material for Computer Aided Verification (CAV) papers. It details how to separate decision-critical content (main theorems, proof ideas) into the primary body, while moving voluminous details (full proofs, massive benchmarks) to the optional appendix or artifact, ensuring the paper remains judgeable from the main text alone.

This detailed guide outlines the structure and scientific rigor required for publishing high-quality papers in the domain of Computer Aided Verification (CAV). It instructs authors to transition their work from mere system demonstrations or benchmark reports to formally guaranteed, mathematically precise contributions, emphasizing clear scope definition, stated theorems, and proof ideas.

A comprehensive guide for designing, auditing, and reporting security experiments for top-tier conferences like ACM CCS. It emphasizes moving beyond simple benchmarks by mapping every security claim to undeniable evidence, including exploit runs on named targets, overhead measurements, and evaluation against adaptive attackers. Used for rigorous academic research defense.

A comprehensive guide for authors targeting high-impact chemical engineering venues. It helps authors re-frame their work from simple material synthesis to demonstrating an applied advance with clear mechanistic insight. Focus areas include catalysis, separation, functional materials, and performance benchmarking under realistic process conditions.

Comprehensive guidelines for conducting rigorous empirical validation in financial forecasting and econometrics manuscripts. This framework covers essential steps such as ensuring data provenance, performing robust out-of-sample testing (rolling windows), establishing multi-layered benchmark models, and conducting significance tests (e.g., DM/MCS). It helps authors meet the high academic standards required for top-tier journals.