ICLR Skills

Use when planning an ICLR project timeline from topic selection through OpenReview submission, discussion, revision, decision, camera-ready, poster, video, and public artifact release. Use when sequencing milestones against the current cycle's OpenReview deadlines, budgeting time for the long public discussion phase, or assigning owners for anonymity audits and reviewer-verifiable evidence paths.

Use when auditing an ICLR main-conference submission for OpenReview readiness, double-blind anonymity, page limits, reciprocal reviewing, LLM-use disclosure, dual-submission policy, supplementary material, and desk-reject risk.

Use when designing or auditing ICLR experiments, including baselines, ablations, scaling laws, robustness, statistics, benchmarks, human evaluation, and compute reporting. Use when a reviewer questions whether a representation-learning or model gain is real, when you must isolate one mechanism with an ablation, or when preparing a small compute-matched control that can be posted inline during the public discussion period.

Use when preparing an accepted ICLR paper for camera-ready submission, OpenReview metadata, poster instructions, slide deck, project page, video, and publication logistics. Use when de-anonymizing the final PDF, folding in promised discussion-period fixes, confirming the camera-ready page allowance for the current cycle, or assembling poster and presentation assets for ICLR's poster-heavy program.

Use when positioning an ICLR paper against prior work, concurrent OpenReview submissions, arXiv papers, benchmark lineages, and adjacent learning-representation claims. Use when a reviewer cites a paper you missed, when a public comment disputes your novelty, or when separating "shares a component with" from "solves the same representation-learning problem" so the claim survives permanent public scrutiny.

Use when organizing ICLR appendices, supplementary files, anonymous code/data, revised PDFs, private links, and discussion-period updates under OpenReview rules. Use when deciding what stays in the main text versus the appendix, how to label a discussion-period revision so reviewers find changes fast, or how to package anonymous artifacts that remain part of the permanent public record after acceptance.

Use when revising an ICLR manuscript for learning-representation framing, OpenReview readability, contribution clarity, limitations, ethics, and reviewer navigation. Use when the core representation insight is buried, when an abstract must read well as an OpenReview snippet, or when adding a "what to verify" path so reviewers can confirm the claim under permanent public review.

Use when explaining or planning around the ICLR review process, including OpenReview public review, AC/SAC roles, reviewer questions, discussion, revisions, final recommendations, and ethics escalation. Use when deciding whether to reply publicly or privately, how a permanently visible review thread shapes strategy, when to upload an allowed revision, or when to escalate a defective review to the AC for the current cycle.

Use when drafting ICLR OpenReview author discussion replies, revision notes, public comments, restricted comments, and responses to reviewer or AC concerns during the discussion period. Use when reviews land and you must decide what to answer publicly versus privately, how to log a discussion-period revision, or how to move a borderline score before the AC writes the meta-review for the permanent record.

Use when strengthening reproducibility for ICLR papers, including seeds, variance, compute, datasets, implementation details, ethics statements, and reviewer-verifiable evidence. Use when writing the ICLR reproducibility statement, when a reviewer says a result is not verifiable, or when mapping each representation-learning claim to a seed, split, and command so anyone reading the permanent OpenReview record can check it.

Use when deciding whether a project is a strong ICLR submission, should be reframed for ICLR, or should be routed to NeurIPS, ICML, AAAI, AISTATS, ACL, CVPR, KDD, or another venue. Use when a project lacks a clear representation-learning insight, when an application result needs a learning contribution to fit ICLR, or when weighing ICLR's deep-learning center of gravity against a better-matched venue.

Use when packaging ICLR code, data, checkpoints, demos, logs, and reproduction instructions for reviewers or post-acceptance release, including anonymized links and private OpenReview discussion-period sharing. Use when a reviewer asks for a missing repro path, when a public comment questions whether claims can be verified, or when converting an anonymous supplement into a durable post-acceptance release for the permanent ICLR record.