--- name: cikm-review-process description: Use when reasoning about CIKM peer review — the EasyChair double-blind pipeline, the mixed IR/data-mining/knowledge-management reviewer pool, per-track evaluation criteria, the ACM Peer Review Policy including the no-AI-written-reviews rule, notification timing, and what actually moves borderline decisions. --- # CIKM Review Process CIKM review runs on EasyChair under double-blind rules, inside the ACM Peer Review Policy, and — its defining feature — in front of a reviewer pool drawn from three communities at once. Verified 2026 mechanics (source map, 2026-07-08): submissions closed in May/June, notification lands August 7, and referees are explicitly barred from using AI systems to write reviews. Whether 2026 includes an author response window is unconfirmed in either direction (待核实); plan without assuming one. ## The blended-pool effect A CIKM paper is typically read by people whose default standards differ: | Reviewer's home lane | What they instinctively grade | Complaint they file most | |---|---|---| | Information retrieval | Evaluation design, baselines, metric discipline | "Baselines are stale / significance untested" | | Data mining | Mechanism novelty, scalability, ablation logic | "Delta over the nearest KDD-line method unclear" | | KM / databases | Data model, integration cost, system realism | "Would not survive real schema/scale/noise" | The practical consequence: a paper optimized for one lane can draw its harshest review from another. Write the submission so each lane finds its own checklist satisfied — a defensible evaluation, an isolated mechanism, *and* a credible data story — or explicitly scope the claim to the lanes it serves. This is also why the reviewer-pool breadth rewards two-lane papers (see `cikm-topic-selection`): they give more of the panel a reason to champion. ## Per-track criteria shift The five tracks are judged against different success definitions. Full Research is graded on novelty plus evidence depth; Short Research on the sharpness of a single finding, not breadth; Applied Research on the credibility of deployment evidence (launch, data release) and transferable lessons; Resource on documentation, licensing, and likely reuse; Demonstration on what a visitor can actually do with the prototype. Reviewing a submission against the wrong track's bar — the most common self-review error — produces both false confidence and false alarm. ## What moves a borderline - **A champion, not an average.** With three lanes in the room, a decided advocate who says "my community needs this" outweighs a slightly higher mean score. - **Unanswered lane-specific objections sink.** A mining reviewer's unaddressed scalability question reads as a gap even if both IR reviewers scored high. - **Compliance is upstream of merit.** The 2026 desk-reject set — missed reviewer nomination, undeclared public version, budget or anonymity violations, missing GenAI disclosure — removes papers before any lane weighs in. - **Chairs calibrate across tracks.** Meta-decisions reconcile the lanes; a review that misread the track's bar can be discounted at that level, which is why a polite confidential-comments note on track fit (where the form allows one) is occasionally decisive. ## Timeline realism for the live cycle Between the June close and the August 7 notification there is no author-visible activity by default. Do not read silence as signal; do not email chairs for status; do use the window as `cikm-workflow` Mode A prescribes (artifact readiness, camera-ready pre-drafting). If a response phase is announced mid-cycle, it will be short — pre-agree within the team who drafts and who signs off. ## Reading a CIKM review packet When reviews arrive, decode them by lane before reacting: ```text For each review: 1. Identify the lane from the vocabulary ("nDCG/baselines/collections" → IR; "novelty/ablation/scale" → mining; "schema/provenance/real data" → KM-DB) 2. Separate lane-standard demands (must answer) from lane-mismatch complaints (may be a track/framing misread) 3. Rank objections by whether a chair would treat them as blocking: correctness > missing decisive evidence > positioning > polish ``` Two panel patterns worth recognizing. **Split-by-lane scores** (one lane high, one low) usually mean the paper is legible to only part of the panel — a framing problem more than an evidence problem, fixable at the next venue with `cikm-writing-style`. **Uniform middling scores** usually mean the contribution is understood and judged thin — an evidence problem no rewrite fixes. ## Confidentiality and integrity boundaries The ACM Peer Review Policy governs both directions. Authors must not attempt reviewer identification, contact reviewers, or publicize review text with intent to pressure; reviewers must keep submissions confidential and — a 2026-explicit rule — must not have AI systems write their reviews. If a review appears AI-generated or plainly template-pasted, the recourse is a factual, unemotional note to the program chairs, not a public thread. Chairs can and do recalibrate around a defective review; they cannot around an author who breached process. ## After the decision Accepted papers move to `cikm-camera-ready` with an August 20 camera-ready gate — thirteen days after notification, one of the tightest turnarounds in the family. Rejected papers should mine the tri-lane reviews for routing information: lane- specific objections point to the venue whose community wrote them, which is exactly the input `cikm-workflow`'s fallback ring consumes. ## Scale realities CIKM is one of the largest venues in its family — its proceedings run to many hundreds of papers across tracks (exact counts per edition: check the ACM DL record; 2026 statistics 待核实) — which shapes review dynamics in ways small selective venues do not: reviewer load is high, so the first page carries even more of the verdict (`cikm-writing-style`); topical match between paper and reviewer is looser than at a single-community venue, which is why the abstract's lane-vocabulary steers bidding; and per-track sub-committees (applied, resource, demo) apply genuinely different rubrics rather than one program committee's taste. Acceptance statistics for the current cycle were not verifiable (待核实) — do not quote a rate to calibrate hopes; calibrate on whether each lane's blocking question has an answer inside the PDF. ## Output format ```text [Stage] pre-notification / notified-accept / notified-reject / response-window(if any) [Lane read] IR / mining / KM-DB — likely stance of each on this paper [Sharpest objection] [Compliance state] clean / at-risk (which trigger) [Next move] ```