Research, academic papers, education, and exam prep (4.3K skills)
Turn a vague research direction into a focused proposal and a detailed experiment roadmap. This workflow connects method refinement with validation planning, producing review records, an experiment tracker, and a summary of what to run next.
Check mathematical proofs in LaTeX with review by a separate AI model. Identify missing assumptions and unsupported steps, explore counterexamples, repair derivations, and keep an audit record of issues, changes, and follow-up reviews.
Develop mathematical proofs for machine learning and AI theory by checking assumptions, intermediate results, and edge cases. Produce a complete proof, a corrected claim with its proof, or a report explaining the unresolved gap.
Plan and run ablation studies, experiments that remove or replace parts of a method to test their contribution. Organize reviewer questions, priorities, expected outcomes, and compute costs, then record results and findings.
Check experimental results before making research claims. A fresh reviewer examines reference data, score calculations, result files, and evaluation scope, then produces an evidence-based report showing which claims are supported and which need qualification.
Organize paper reviews into individual concerns and draft responses within venue limits. Track evidence, unanswered issues, and approved commitments, then produce reviewer replies and a revision checklist that can be updated during follow-up rounds.
Audit citations in a LaTeX paper against online sources. Verify that references exist, bibliographic details are accurate, and each cited work supports the surrounding claim, then produce reports with findings and proposed corrections.
Turn research ideas and literature into a structured funding proposal. Supports KAKENHI and other funding programs, guiding literature positioning, research aims, methods, timelines, and budget justification, with external review to identify weaknesses in the draft.
Check paper numbers, comparisons, and evaluation scope against raw experiment results. A fresh reviewer from a different model family examines only source evidence, then produces reports identifying rounding issues, inconsistent settings, aggregation errors, and unsupported claims.
Turn a research proposal into an experiment plan tied to specific paper claims. Define comparisons, evaluation measures, tests that remove individual components, run order, and compute estimates, with a written plan and tracker for execution.
Search arXiv papers by topic or paper ID, review authors and abstracts, and summarize contributions stated in the abstracts. Download selected papers or all search results as PDFs to build a local paper library.
Improve research through up to four rounds of external language model review, fixes, and reassessment. Use an OpenAI-compatible API to identify weaknesses, prioritize changes, and keep a cumulative record of reviewer responses, actions, and experiment results.
Run repeated reviews of research work, implement fixes, and request another assessment for up to four rounds by default. The workflow records progress and stops when both the review score and verdict meet its acceptance rules.
Explore a broad research direction through literature searches, idea generation, novelty checks, small pilot experiments, and critical review. Receive ranked ideas plus a refined proposal and experiment plan for the leading candidate, with evidence recorded across stages.
Review communications research by searching existing libraries and notes before academic databases. Produce a paper comparison table covering methods, results, and limitations, then summarize major approaches, evidence strength, and remaining research gaps.
Stress-test a paper with a focused rejection argument, then have a separate reviewer assess each objection against the current text. The report identifies unresolved weaknesses, their severity, and priority fixes for submission or rebuttal preparation.
Search open-access academic papers through DeepXiv, then move from brief summaries to section outlines and individual sections. Find trending papers and focus reading on the parts needed to answer a research question.
Checks a research idea against recent literature, comparing its main claims with the closest published work. Produces a structured report explaining overlap, meaningful differences, risks, and whether to proceed, proceed cautiously, or abandon the idea.
Use MiniMax to review research, address weaknesses through code changes, experiments, or analysis, and request another assessment. The workflow runs up to four rounds and preserves reviewer responses, actions, results, and unresolved blockers.
Understand a single arXiv paper through an AlphaXiv summary, with full text and LaTeX source available when needed. Use it to identify key findings or ask specific questions about methods, experiments, and equations.
Use Gemini to discover research papers through related problems and alternative terminology. Results are organized into a table with publication details, code links, and short summaries, with filters for year, venue, and available code.
Plan ablation studies, experiments that remove or replace parts of a machine learning method to test their contribution. Define reviewer questions, expected outcomes, priorities, execution order, and compute estimates, then implement experiments and record findings.
Audit research experiments before using their results to support claims. An independent model checks reference data, metric calculations, result files, and evaluation scope, producing reports with evidence, findings, and specific actions to address concerns.
Turn scattered research equations and notes into a structured derivation document. Clarify the target, assumptions, and notation; distinguish exact steps from approximations and interpretations; and report missing conditions or logical gaps when the derivation cannot be supported.