Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› colt-review-process
colt-review-process
GitHub指导COLT论文投稿与审稿策略。涵盖双盲机制、证明优先的评估标准（正确性、显著性等）、反辩阶段要点及PMLR出版流程，帮助作者应对理论机器学习领域的严格同行评审。
Trigger Scenarios
询问COLT投稿截止日期或流程
分析COLT审稿评分维度
制定COLT论文反辩策略
了解COLT双盲审稿细节
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-review-process -g -y
SKILL.md
Frontmatter
{
"name": "colt-review-process",
"description": "Use when reasoning about COLT (Conference on Learning Theory) peer review — the double-anonymous-with-informed-area-chair model, correctness-first evaluation by expert theorists, the rebuttal stage before decisions, single-track acceptance stakes, and how PMLR publication and the community's proof culture shape outcomes."
}
COLT Review Process
Use this to plan around COLT's review pipeline. Process facts below were verified against the COLT 2026 CFP on 2026-07-08; mechanics are re-decided by each edition's program chairs, so reconfirm in the cycle you are in.
The 2026 pipeline
- Submission via Microsoft CMT by February 4, 2026 (AoE) for the 39th edition.
- Double-anonymous refereeing with a twist: reviewers do not see author names, but the area chair handling the paper does, and may reveal identities to a reviewer during the rebuttal period on request when needed for a proper review.
- Initial reviews go to authors before decisions; a rebuttal window follows.
- Accepted papers appear in PMLR (v291 carried COLT 2025; the 2026 volume number is assigned at publication).
- COLT is run by the Association for Computational Learning, with program chairs rotating yearly; the 2026 chair names were not published in a form verifiable at the access date (待核实 — check learningtheory.org/colt2026/).
Who reviews a COLT paper
The pool is learning theorists: statistical learning, online learning and bandits, optimization theory, RL theory, privacy, and adjacent CS theory. Practical consequences:
- Expect at least one reviewer working within epsilon of your subfield who will actually verify proofs, not skim them.
- "Standard techniques" claims get adjudicated by people who know the standard techniques' exact reach — bluffing about novelty of technique fails here faster than anywhere else in ML.
- Empirical framing earns nothing by itself; a reviewer may value an illustrative experiment, but no reviewer will accept one in lieu of a proof.
What the scores actually track
| Dimension | Raises it | Sinks it |
|---|---|---|
| Correctness | Complete proofs, tracked constants, checked external results | One irreparable gap — usually terminal regardless of other strengths |
| Significance | Resolving a known open question; improving a known rate; a clean new model | A bound in a model nobody asked about, unmotivated |
| Novelty of technique | An argument that visibly cannot be assembled from known parts | A reduction the reviewer completes in the margin |
| Tightness / completeness | Matching upper and lower bounds; explicit regime coverage | Upper bound only, gap to the known lower bound unexamined |
| Clarity | Formal setup early, roadmap per proof, stable notation | Definitions scattered; appendix required to parse the theorem |
Decision dynamics
- One sustained correctness objection outweighs any number of enthusiasm points; the AC's first job is deciding whether the proofs close.
- Because the AC knows author identities, arguments in rebuttal should stand on mathematics alone — appeals to seniority or track record are visible and land badly.
- Significance disputes ("who cares about this model?") are where rebuttals genuinely move decisions: a crisp paragraph tying the model to a named prior line, an open problem, or an empirical phenomenon can flip a fence-sitter.
- COLT is single-track and relatively small; acceptance implies your talk enters the entire community's field of view, and the bar reflects that.
- Some editions have used an "accept with minor revisions verified by the AC" flavor of shepherding for fixable issues; whether the current cycle does is announced with decisions (待核实).
Reading a COLT review
Review anatomy — where the decision signal lives:
1. Summary paragraph -> did the reviewer parse the model correctly?
If not, your rebuttal's first job is the misread.
2. "Detailed comments" -> line-numbered proof remarks; each is a verification
trace. Silence about App. C means C was not read.
3. Questions to authors -> the actual decision hinges; answer these first.
4. Typo list -> free labor; acknowledge briefly, fix silently.
Reviews that engage deeply with the proofs are good news even when negative — the paper was taken seriously, and precise objections are answerable. The dangerous review is the short, high-level one; it signals the significance case never landed, and that is a framing problem the rebuttal must solve.
After the decision: reading the outcome
- Accept: the reviews still matter — camera-ready promises made in rebuttal are
expected in the final PDF, and the AC may check (
colt-camera-readykeeps the ledger). - Reject with proof-level reviews: you received a free verification report from three experts. Patch the mathematics, then choose between the next COLT, ALT (the same community's sister venue, roughly anti-phased deadline), or a journal if the fixed version grew.
- Reject with significance objections only: the theorems survived; the framing died. Rebuild the known-vs-new ledger and the model-motivation paragraph before resubmitting anywhere — the same reviewers may see it again in a small community.
- Reject you believe is wrong: there is no formal appeals process to rely on; the productive channel is a stronger paper, since the community re-reviews resubmissions on their mathematics.
Confidentiality and conduct
- Submissions are confidential; reviewers may not use or share the results before publication.
- COLT publishes a code of conduct for participants (a 2026 page exists at learningtheory.org); professional-conduct expectations extend to the rebuttal tone.
- Reviewer conflicts run through CMT domain and coauthor declarations — enter them completely, since the informed-AC model depends on accurate conflict data.
- Discussing your submission publicly (talks, social media) during review is not forbidden by anonymity rules aimed at reviewers, but volume-seeking publicity during the review window is poor form in a community this small.
Cycle-volatility warnings
- Portal, anonymity mechanics, rebuttal format, shepherding, and decision timeline are annual decisions. Decision dates for 2026 were not on the pages checked (待核实).
- Reviewer-volunteering expectations for submitting authors have not been a stated COLT policy in the verified material; do not assume one either way without the current CFP.
Output format
[Stage] pre-submission / under review / rebuttal / decision
[Score driver] correctness / significance / technique / tightness / clarity
[Reviewer engagement] deep proof-level / shallow-summary (framing failed)
[Rebuttal leverage] <the one thread that can move the decision>
[Process facts to reconfirm] <current-cycle items still 待核实>
Version History
- 9f86f09 Current 2026-07-19 14:41
