Prepare artifact-evaluation submissions for ML and systems conferences, including artifact scope, installability, reproducibility claims, hardware needs, badges, expected runtime, and reviewer instructions.
Audit experiments for top ML, CV, systems, and architecture submissions, checking baselines, ablations, metrics, statistical strength, datasets, stress tests, failure cases, and claim coverage.
Build a top-conference paper strategy from raw results, including central claim, contribution framing, section outline, reviewer value proposition, and claim-evidence alignment.
Plan a top-conference rebuttal or author response by clustering reviewer comments, identifying misunderstandings, concessions, evidence, response priority, and camera-ready promises.
Simulate critical top-conference reviewers for a draft, producing likely strengths, weaknesses, score drivers, clarity objections, missing experiments, novelty concerns, and rebuttal-preparation notes.
Build and audit conference submission checklists for paper PDF, supplement, OpenReview/CMT metadata, anonymity, ethics, broader impact, reproducibility, author forms, and deadline readiness.
End-to-end top-conference submission assistant for NeurIPS, ICLR, ICML, MLSys, ASPLOS, and CVPR-style papers. Use for submission planning, contribution framing, experiment gap analysis, paper outline, related work positioning, checklist compliance, rebuttal strategy, artifact evaluation, and camera-ready readiness.
Evaluate whether a research paper fits NeurIPS, ICLR, ICML, MLSys, ASPLOS, CVPR, or adjacent venues based on contribution type, evidence, audience, novelty, experiment norms, and reviewer expectations.