# Colt Review Process > 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. - Skill: `thedixitjain/colt-review-process` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/colt-review-process` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/colt-review-process/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/colt-review-process --- # 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 ```text 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-ready` keeps 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 ```text [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] [Process facts to reconfirm] ``` --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `COLT-Skills/skills/colt-review-process/SKILL.md`